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Kondo Y, Bando K, Suzuki I, Miyazaki Y, Nishida D, Hara T, Kadone H, Suzuki K. Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2250-2260. [PMID: 38865235 DOI: 10.1109/tnsre.2024.3413055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson's disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. We attempted to establish an automatic FoG detection method from videos taken in uncontrolled environments such as in daily clinical practices. Motion features of 16 patients were extracted from timed-up-and-go test in 109 video data points, through object tracking and three-dimension pose estimation. These motion features were utilized to form the FoG detection model, which combined rule-based and machine learning-based models. The rule-based model distinguished the frames in which the patient was walking from those when the patient has stopped, using the pelvic position coordinates; the machine learning-based model distinguished between FoG and stop using a combined one-dimensional convolutional neural network and long short-term memory (1dCNN-LSTM). The model achieved a high intraclass correlation coefficient of 0.75-0.94 with a manually-annotated duration of FoG and %FoG. This method is novel as it combines object tracking, 3D pose estimation, and expert-guided feature selection in the preprocessing and modeling phases, enabling FoG detection even from videos captured in uncontrolled environments.
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Zhang W, Sun H, Huang D, Zhang Z, Li J, Wu C, Sun Y, Gong M, Wang Z, Sun C, Cui G, Guo Y, Chan P. Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease. Neurol Sci 2024; 45:431-453. [PMID: 37843692 DOI: 10.1007/s10072-023-07017-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 08/06/2023] [Indexed: 10/17/2023]
Abstract
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Hong Sun
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Debin Huang
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Zixuan Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Jinyu Li
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chan Wu
- Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, 100029, China
| | - Yingying Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Mengyi Gong
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Zhi Wang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chao Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.
| | - Yuzhu Guo
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Piu Chan
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China.
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Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiol Meas 2023; 44:12TR01. [PMID: 38061062 DOI: 10.1088/1361-6579/ad133b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/07/2023] [Indexed: 12/27/2023]
Abstract
This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.
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Affiliation(s)
- Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | | | - Maurizio Schmid
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | - Simone Ranaldi
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
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Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
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Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Bansal SK, Basumatary B, Bansal R, Sahani AK. Techniques for the detection and management of freezing of gait in Parkinson's disease - A systematic review and future perspectives. MethodsX 2023; 10:102106. [PMID: 36942282 PMCID: PMC10023964 DOI: 10.1016/j.mex.2023.102106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/01/2023] [Indexed: 03/07/2023] Open
Abstract
Freezing of Gait (FoG) is one of the most critical debilitating motor symptoms of advanced Parkinson's disease (PD) with a higher rate of occurrence in aged people. PD affects the cardinal motor functioning and leads to non-motor symptoms, including cognitive and neurobehavioral abnormalities, autonomic dysfunctions and sleep disorders. Since its pathogenesis is complex and unclear yet, this paper targets the studies done on the pathophysiology and epidemiology of FoG in PD. Gait disorder and cardinal features vary from festination (involuntary hurrying in walking) to freezing of gait (breakdown of repetitive movement of steps despite the intention to walk) in patients. Hence, it is difficult to assess the FoG in clinical trials. Therefore, the current research emphasizes wearable sensor-based systems over pharmacology and surgical methods.•This paper presents a technological review of various techniques used for the assessment of FoG with a comprehensive comparison.•Researchers are aiming at the development of wireless sensor-based assistive devices to (a) predict the FoG episode in a different environment, (b) acquire the long-term data for real-time analysis, and (c) cue the FoG patients.•We summarize the work done till now and future research directions needed for a suitable cueing mechanism to overcome FoG.
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Affiliation(s)
- Sunil Kumar Bansal
- Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
- Department of Electrical and Instrumentation Engineering, SLIET Longowal, Sangrur, India
| | - Bijit Basumatary
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
- Corresponding author.
| | - Rajinder Bansal
- Department of Neurology, Dayanand Medical College and Hospital, Ludhiana, India
| | - Ashish Kumar Sahani
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
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Huang T, Li M, Huang J. Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review. Front Aging Neurosci 2023; 15:1119956. [PMID: 36875701 PMCID: PMC9975590 DOI: 10.3389/fnagi.2023.1119956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Background The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way. Objective This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD. Methods Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance. Results A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection. Conclusion These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.
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Affiliation(s)
- Tinghuai Huang
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Meng Li
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Jianwei Huang
- Department of Gastroenterology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
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Guo CC, Chiesa PA, de Moor C, Fazeli MS, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. J Med Internet Res 2022; 24:e37683. [DOI: 10.2196/37683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/18/2022] [Accepted: 10/11/2022] [Indexed: 11/22/2022] Open
Abstract
Background
With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.
Objective
We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.
Methods
A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers.
Results
A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles).
Conclusions
Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.
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Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:healthcare10101940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
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Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
- Correspondence: (A.A.I.); or (A.O.B.)
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
- Correspondence: (A.A.I.); or (A.O.B.)
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
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Atri R, Urban K, Marebwa B, Simuni T, Tanner C, Siderowf A, Frasier M, Haas M, Lancashire L. Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22186831. [PMID: 36146181 PMCID: PMC9502239 DOI: 10.3390/s22186831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/25/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2023]
Abstract
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.
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Affiliation(s)
- Roozbeh Atri
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Kevin Urban
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Barbara Marebwa
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Caroline Tanner
- Department of Neurology, Weill Institute for Neurosciences University of California, San Francisco, CA 94143, USA
- Parkinson’s Disease Research Education and Clinical Center, San Francisco Veteran’s Affairs Medical Center, San Francisco, CA 94121, USA
| | - Andrew Siderowf
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark Frasier
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Magali Haas
- Cohen Veterans Bioscience, New York, NY 10018, USA
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11
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Kim H, Park C, You J(SH. Concurrent validity, test-retest reliability, and sensitivity of a PostureRite system measurement on dynamic postural sway and risk of fall in cerebral palsy. NeuroRehabilitation 2022; 51:151-159. [DOI: 10.3233/nre-210331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: Accurately diagnosing dynamic postural sway (DPS) is essential for effective and sustainable intervention in children with cerebral palsy (CP). We developed an accurate, inexpensive, and wearable DPS measurement system to measure DPS accurately and consistently during walking and functional activities of daily living. OBJECTIVE: We investigated the validity and reliability of this PostureRite system in children with CP, and the link between PostureRite and clinical measures including gross motor function measure (GMFM), pediatric balance scale (PBS), and fall efficacy scale (FES). METHODS: Twenty-one participants were categorized as follows: 11 healthy adults (3 females, mean age, 25.00±1.00 years) and 10 children with CP (mean age, 11.10±6.28 years). We determined the concurrent validity of PostureRite by comparing DPS data to the gold standard accelerometer measurement results. We determined test-retest reliability by measuring DPS data on three occasions at 2-h intervals. We assessed PostureRite measurement sensitivity to ascertain differences between healthy children and children with CP DPS measurements. RESULTS: Random and mixed intraclass correlation coefficients (ICC2,k and ICC3,k) were obtained; an independent T-test was performed (P < 0.05). Concurrent validity analysis showed a good relationship between the gold standard accelerometer and PostureRite (ICC2,k = 0.973, P < 0.05). Test-retest reliability demonstrated a good relationship across the three repeated measures of the DPS data (ICC3,k = 0.816–0.924, P < 0.05). Independent T-test revealed a significant difference in DPS data between healthy adults and children with CP (P < 0.05). CONCLUSIONS: We developed a portable, wireless, and affordable PostureRite system to measure DPS during gross motor function associated with daily activity and participation, and established the concurrent validity, test-retest reliability as sensitivity, and clinical relevance by comparing the DPS obtained from the participants with and without CP.
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Affiliation(s)
- Heejun Kim
- Sports Movement Artificial Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
- Department of Physical Therapy, Yonsei University, Wonju, Republic ofKorea
| | - Chanhee Park
- Sports Movement Artificial Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
- Department of Physical Therapy, Yonsei University, Wonju, Republic ofKorea
| | - Joshua (Sung) H. You
- Sports Movement Artificial Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
- Department of Physical Therapy, Yonsei University, Wonju, Republic ofKorea
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12
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Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
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Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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Yang B, Li Y, Wang F, Auyeung S, Leung M, Mak M, Tao X. Intelligent wearable system with accurate detection of abnormal gait and timely cueing for mobility enhancement of people with Parkinson's disease. WEARABLE TECHNOLOGIES 2022; 3:e12. [PMID: 38486907 PMCID: PMC10936378 DOI: 10.1017/wtc.2022.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/11/2022] [Accepted: 05/25/2022] [Indexed: 03/17/2024]
Abstract
Previously reported wearable systems for people with Parkinson's disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37 s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.
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Affiliation(s)
- Bao Yang
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ying Li
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
| | - Fei Wang
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- School of Textile Materials and Engineering, Wuyi University, Jiangmen, China
| | - Stephanie Auyeung
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Manyui Leung
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
| | - Margaret Mak
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xiaoming Tao
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
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Filtjens B, Ginis P, Nieuwboer A, Slaets P, Vanrumste B. Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks. J Neuroeng Rehabil 2022; 19:48. [PMID: 35597950 PMCID: PMC9124420 DOI: 10.1186/s12984-022-01025-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 05/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. METHODS Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. RESULTS The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. CONCLUSIONS The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.
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Affiliation(s)
- Benjamin Filtjens
- Department of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium. .,Department of Mechanical Engineering, Intelligent Mobile Platforms Research Group, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
| | - Pieter Ginis
- Department of Rehabilitation Sciences, Research Group for Neurorehabilitation (eNRGy), KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, Research Group for Neurorehabilitation (eNRGy), KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Peter Slaets
- Department of Mechanical Engineering, Intelligent Mobile Platforms Research Group, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Bart Vanrumste
- Department of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
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Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis. SENSORS 2022; 22:s22103700. [PMID: 35632109 PMCID: PMC9148133 DOI: 10.3390/s22103700] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
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16
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Pardoel S, Nantel J, Kofman J, Lemaire ED. Prediction of Freezing of Gait in Parkinson's Disease Using Unilateral and Bilateral Plantar-Pressure Data. Front Neurol 2022; 13:831063. [PMID: 35572938 PMCID: PMC9101469 DOI: 10.3389/fneur.2022.831063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). FOG has been linked to falling, injury, and overall reduced mobility. Wearable sensor-based devices can detect freezes already in progress and provide a cue to help the person resume walking. While this is helpful, predicting FOG episodes before onset and providing a timely cue may prevent the freeze from occurring. Wearable sensors mounted on various body parts have been used to develop FOG prediction systems. Despite the known asymmetry of PD motor symptom manifestation, the difference between the most affected side (MAS) and least affected side (LAS) is rarely considered in FOG detection and prediction studies. Methods To examine the effect of using data from the MAS, LAS, or both limbs for FOG prediction, plantar pressure data were collected during a series of walking trials and used to extract time and frequency-based features. Three datasets were created using plantar pressure data from the MAS, LAS, and both sides together. ReliefF feature selection was performed. FOG prediction models were trained using the top 5, 10, 15, 20, 25, or 30 features for each dataset. Results The best models were the MAS model with 15 features and the LAS and bilateral models with 5 features. The LAS model had the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MAS model achieved the highest specificity (84.9%) and lowest false positive rate (1.9 false positives/walking trial). Overall, the bilateral model was best with 77.3% sensitivity and 82.9% specificity. In addition, the bilateral model identified 94.2% of FOG episodes an average of 0.8 s before FOG onset. Compared to the bilateral model, the LAS model had a higher false positive rate; however, the bilateral and LAS models were similar in all the other evaluation metrics. Conclusion The LAS model would have similar FOG prediction performance to the bilateral model at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased false positive rate may be acceptable to people with PD. Therefore, a single plantar pressure sensor placed on the LAS could be used to develop a FOG prediction system and produce performance similar to a bilateral system.
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Affiliation(s)
- Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada
- *Correspondence: Julie Nantel
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Edward D. Lemaire
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Centre for Rehabilitation Research and Development, Ottawa Hospital Research Institute, Ottawa, ON, Canada
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Ribeiro De Souza C, Miao R, Ávila De Oliveira J, Cristina De Lima-Pardini A, Fragoso De Campos D, Silva-Batista C, Teixeira L, Shokur S, Mohamed B, Coelho DB. A Public Data Set of Videos, Inertial Measurement Unit, and Clinical Scales of Freezing of Gait in Individuals With Parkinson's Disease During a Turning-In-Place Task. Front Neurosci 2022; 16:832463. [PMID: 35281510 PMCID: PMC8904564 DOI: 10.3389/fnins.2022.832463] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/24/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Caroline Ribeiro De Souza
- Human Motor Systems Laboratory, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | - Runfeng Miao
- BIOROB Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Júlia Ávila De Oliveira
- Human Motor Systems Laboratory, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | | | - Débora Fragoso De Campos
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Carla Silva-Batista
- Exercise Neuroscience Research Group, School of Arts, Sciences, and Humanities, University of São Paulo, São Paulo, Brazil
| | - Luis Teixeira
- Human Motor Systems Laboratory, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | - Solaiman Shokur
- BIOROB Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Excellence in Robotics and AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Bouri Mohamed
- BIOROB Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Boari Coelho
- Human Motor Systems Laboratory, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- Biomedical Engineering, Federal University of ABC, São Paulo, Brazil
- *Correspondence: Daniel Boari Coelho
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18
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AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR. Application of artificial intelligence in wearable devices: Opportunities and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106541. [PMID: 34837860 DOI: 10.1016/j.cmpb.2021.106541] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/07/2021] [Accepted: 11/15/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data. METHODS Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives. RESULTS Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays. CONCLUSION The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.
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Affiliation(s)
- Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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20
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Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. SENSORS (BASEL, SWITZERLAND) 2021; 21:6446. [PMID: 34640763 PMCID: PMC8512068 DOI: 10.3390/s21196446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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Affiliation(s)
- Tahjid Ashfaque Mostafa
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Sara Soltaninejad
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Tara L. McIsaac
- Arizona School of Health Sciences, A.T. Still University, 5850 E. Still Circle, Mesa, AZ 85206, USA;
- School of Pharmacy and Health Professions, Creighton University Health Sciences, 3100 N. Central Ave., Phoenix, AZ 85013, USA
| | - Irene Cheng
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
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21
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Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task. SENSORS 2021; 21:s21082661. [PMID: 33920070 PMCID: PMC8069332 DOI: 10.3390/s21082661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/31/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.
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22
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Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data. SENSORS 2021; 21:s21062246. [PMID: 33806984 PMCID: PMC8004667 DOI: 10.3390/s21062246] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/17/2022]
Abstract
Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.
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23
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Davids J, Ashrafian H. AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_190-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Morris R, Mancin M. Lab-on-a-chip: wearables as a one stop shop for free-living assessments. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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25
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Artusi CA, Imbalzano G, Sturchio A, Pilotto A, Montanaro E, Padovani A, Lopiano L, Maetzler W, Espay AJ. Implementation of Mobile Health Technologies in Clinical Trials of Movement Disorders: Underutilized Potential. Neurotherapeutics 2020; 17:1736-1746. [PMID: 32734442 PMCID: PMC7851293 DOI: 10.1007/s13311-020-00901-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Mobile health technologies (mHealth) are patient-worn or portable devices aimed at increasing the granularity and relevance of clinical measurements. The implementation of mHealth has the potential to decrease sample size, duration, and cost of clinical trials. We performed a review of the ClinicalTrials.gov database using a standardized approach to identify adoption in and usefulness of mHealth in movement disorders interventional clinical trials. Trial phase, geographical area, availability of data captured, constructs of interest, and outcome priority were collected. Eligible trials underwent quality appraisal using an ad hoc 5-point checklist to assess mHealth feasibility, acceptability, correlation with patient-centered outcome measures, and clinical meaningfulness. A total of 29% (n = 54/184) registered trials were using mHealth, mainly in Parkinson's disease and essential tremor (59.3% and 27.8%). In most cases, mHealth were used in phase 2 trials (83.3%) as secondary outcome measures (59.3%). Only five phase 3 trials, representing 9.3% of the total, used mHealth (1 as primary outcome measure, 3 as secondary, and 1 as tertiary). Only 3.7% (n = 2/54) of all trials used mHealth for measuring both motor and non-motor symptoms, and 23.1% (n = 12/52) used mHealth for unsupervised, ecologic outcomes. Our findings suggest that mHealth remain underutilized and largely relegated to phase 2 trials for secondary or tertiary outcome measures. Efforts toward greater alignment of mHealth with patient-centered outcomes and development of a universal, common-language platform to synchronize data from one or more devices will assist future efforts toward the integration of mHealth into clinical trials.
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Affiliation(s)
- Carlo Alberto Artusi
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Andrea Sturchio
- Gardner Family Center for Parkinson's disease and Movement Disorders, Department of Neurology, University of Cincinnati Academic Health Center, 260 Stetson St., Suite 2300, Cincinnati, OH, 45267-0525, USA
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- FERB Onlus, Ospedale S. Isidoro, Trescore Balneario, Bergamo, Italy
| | - Elisa Montanaro
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Leonardo Lopiano
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Alberto J Espay
- Gardner Family Center for Parkinson's disease and Movement Disorders, Department of Neurology, University of Cincinnati Academic Health Center, 260 Stetson St., Suite 2300, Cincinnati, OH, 45267-0525, USA.
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Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects. PARKINSONS DISEASE 2020; 2020:4693019. [PMID: 33029343 PMCID: PMC7530475 DOI: 10.1155/2020/4693019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/07/2020] [Accepted: 07/13/2020] [Indexed: 01/23/2023]
Abstract
Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.
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Reches T, Dagan M, Herman T, Gazit E, Gouskova NA, Giladi N, Manor B, Hausdorff JM. Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4474. [PMID: 32785163 PMCID: PMC7472497 DOI: 10.3390/s20164474] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/06/2020] [Accepted: 08/08/2020] [Indexed: 12/19/2022]
Abstract
Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the "ground-truth" for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.
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Affiliation(s)
- Tal Reches
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
| | - Moria Dagan
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Talia Herman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
| | - Natalia A. Gouskova
- Harvard Medical School, Boston, MA 02115, USA; (N.A.G.); (B.M.)
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA 02131, USA
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Nir Giladi
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Brad Manor
- Harvard Medical School, Boston, MA 02115, USA; (N.A.G.); (B.M.)
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA 02131, USA
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel; (T.R.); (M.D.); (T.H.); (E.G.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
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Pardoel S, Shalin G, Nantel J, Lemaire ED, Kofman J. Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4034-4037. [PMID: 33018884 DOI: 10.1109/embc44109.2020.9176607] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.
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Borzì L, Fornara S, Amato F, Olmo G, Artusi CA, Lopiano L. Smartphone-Based Evaluation of Postural Stability in Parkinson’s Disease Patients During Quiet Stance. ELECTRONICS 2020; 9:919. [DOI: 10.3390/electronics9060919] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
Background: Postural instability is one of the most troublesome motor symptoms of Parkinson’s Disease (PD). It impairs patients’ quality of life and results in high risk of falls. The aim of this study is to provide a reliable tool for the automated assessment of postural instability. Methods: Data acquisition was performed on 42 PD patients and 7 young healthy subjects. They were asked to keep a quiet stance position for at least 30 s while wearing a waist-mounted smartphone. A total number of 414 features was extracted from both time and frequency domain, selected based on Pearson’s correlation, and fed to an optimized Support Vector Machine. Results: The implemented model was able to differentiate patients with mild postural instability from those with severe postural instability and from healthy controls, with 100% accuracy. Conclusion: This study demonstrated the feasibility of using inertial sensors embedded in commercial smartphones and proposed a simple protocol for accurate postural instability scoring. This tool can be used for early detection of PD motor signs, disease follow-up and fall prevention.
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Sigcha L, Costa N, Pavón I, Costa S, Arezes P, López JM, De Arcas G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1895. [PMID: 32235373 PMCID: PMC7181252 DOI: 10.3390/s20071895] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/21/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022]
Abstract
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).
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Affiliation(s)
- Luis Sigcha
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Ignacio Pavón
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Juan Manuel López
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Guillermo De Arcas
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
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Pardoel S, Kofman J, Nantel J, Lemaire ED. Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson's Disease: A Review. SENSORS 2019; 19:s19235141. [PMID: 31771246 PMCID: PMC6928783 DOI: 10.3390/s19235141] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 11/19/2019] [Accepted: 11/20/2019] [Indexed: 12/28/2022]
Abstract
Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.
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Affiliation(s)
- Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Correspondence: ; Tel.: +1-519-888-4567 (ext. 45185)
| | - Julie Nantel
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
| | - Edward D. Lemaire
- Faculty of Medicine, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, ON K1H 8M2, Canada;
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Kwon YT, Lee Y, Berkmen GK, Lim HR, Scorr L, Jinnah HA, Yeo WH. Soft Material-Enabled, Active Wireless, Thin-Film Bioelectronics for Quantitative Diagnostics of Cervical Dystonia. ADVANCED MATERIALS TECHNOLOGIES 2019; 4:1900458. [PMID: 33043125 PMCID: PMC7546326 DOI: 10.1002/admt.201900458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Indexed: 05/25/2023]
Abstract
Recent advances in flexible materials, nanomanufacturing, and system integration have provided a great opportunity to develop wearable flexible hybrid electronics for human healthcare, diagnostics, and therapeutics. However, existing medical devices still rely on rigid electronics with many wires and separate components, which hinders wireless, comfortable, continuous monitoring of health-related human motions. Here, we introduce advanced materials and system integration technologies that enable a soft, active wireless, thin-film bioelectronics. The low-modulus, highly flexible wearable electronic system incorporates a nanomembrane wireless circuit and functional chip components, enclosed by a soft elastomeric membrane. The bioelectronic system offers a gentle, seamless mounting on the skin, while offering a comfortable, highly sensitive and accurate detection of head movements. We utilize the wireless wearable hybrid system for quantitative diagnostics of cervical dystonia (CD) that is characterized by involuntary abnormal head postures and repetitive head movements, sometimes with neck muscle pain. A set of analytical and experimental studies shows a soft system packaging, hard-soft materials integration, and quantitative assessment of physiological signals detected by the SKINTRONICS. In vivo demonstration, involving ten human subjects, captures the device feasibility for use in CD measurement.
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Affiliation(s)
- Young-Tae Kwon
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Yongkuk Lee
- Department of Biomedical Engineering, Wichita State University, Wichita, KS 67260, USA
| | - Gamze Kilic Berkmen
- Departments of Neurology and Human Genetics, School of Medicine, Emory University, GA 30322, USA
| | - Hyo-Ryoung Lim
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Laura Scorr
- Departments of Neurology and Human Genetics, School of Medicine, Emory University, GA 30322, USA
| | - H A Jinnah
- Departments of Neurology and Human Genetics, School of Medicine, Emory University, GA 30322, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Pinto C, Schuch CP, Balbinot G, Salazar AP, Hennig EM, Kleiner AFR, Pagnussat AS. Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait - an analysis using inertial measurement units. J Neuroeng Rehabil 2019; 16:110. [PMID: 31488184 PMCID: PMC6729092 DOI: 10.1186/s12984-019-0579-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 08/19/2019] [Indexed: 11/25/2022] Open
Abstract
Background Impairments of functional mobility may affect locomotion and quality of life in subjects with Parkinson’s disease (PD). Movement smoothness measurements, such as the spectral arc length (SPARC), are novel approaches to quantify movement quality. Previous studies analyzed SPARC in simple walking conditions. However, SPARC outcomes during functional mobility tasks in subjects with PD and freezing of gait (FOG) were never investigated. This study aimed to analyze SPARC during the Timed Up and Go (TUG) test in individuals with PD and FOG. Methods Thirty-one participants with PD and FOG and six healthy controls were included. SPARC during TUG test was calculated for linear and angular accelerations using an inertial measurement unit system. SPARC data were correlated with clinical parameters: motor section of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr scale, Freezing of Gait Questionnaire, and TUG test. Results We reported lower SPARC values (reduced smoothness) during the entire TUG test, turn and stand to sit in subjects with PD and FOG, compared to healthy controls. Unlike healthy controls, individuals with PD and FOG displayed a broad spectral range that encompassed several dominant frequencies. SPARC metrics also correlated with all the above-mentioned clinical parameters. Conclusion SPARC values provide valid and relevant clinical data about movement quality (e.g., smoothness) of subjects with PD and FOG during a functional mobility test. Electronic supplementary material The online version of this article (10.1186/s12984-019-0579-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Camila Pinto
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil.,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | - Clarissa Pedrini Schuch
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil
| | - Gustavo Balbinot
- Brain Institute, Universidade Federal do Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Ana Paula Salazar
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil.,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | - Ewald Max Hennig
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Aline Souza Pagnussat
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil. .,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.
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Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić VS. Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review. Clin Neurol Neurosurg 2019; 184:105442. [PMID: 31351213 DOI: 10.1016/j.clineuro.2019.105442] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/31/2019] [Accepted: 07/11/2019] [Indexed: 01/30/2023]
Abstract
Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.
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Affiliation(s)
- Minja Belić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladislava Bobić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Badža
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Nikola Šolaja
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Đurić-Jovičić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladimir S Kostić
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia.
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Borzì L, Varrecchia M, Olmo G, Artusi CA, Fabbri M, Rizzone MG, Romagnolo A, Zibetti M, Lopiano L. Home monitoring of motor fluctuations in Parkinson’s disease patients. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s40860-019-00086-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson's disease. J Neural Transm (Vienna) 2019; 126:1029-1036. [PMID: 31154512 DOI: 10.1007/s00702-019-02020-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 05/22/2019] [Indexed: 12/14/2022]
Abstract
Objective measurement of walking speed and gait deficits are an important clinical tool in chronic illness management. We previously reported in Parkinson's disease that different types of gait tests can now be implemented and administered in the clinic or at home using Ambulosono smartphone-sensor technology, whereby movement sensing protocols can be standardized under voice instruction. However, a common challenge that remains for such wearable sensor systems is how meaningful data can be extracted from seemingly "noisy" raw sensor data, and do so with a high level of accuracy and efficiency. Here, we describe a novel pattern recognition algorithm for the automated detection of gait-cycle breakdown and freezing episodes. Ambulosono-gait-cycle-breakdown-and-freezing-detection (Free-D) integrates a nonlinear m-dimensional phase-space data extraction method with machine learning and Monte Carlo analysis for model building and pattern generalization. We first trained Free-D using a small number of data samples obtained from thirty participants during freezing of gait tests. We then tested the accuracy of Free-D via Monte Carlo cross-validation. We found Free-D to be remarkably effective at detecting gait-cycle breakdown, with mode error rates of 0% and mean error rates < 5%. We also demonstrate the utility of Free-D by applying it to continuous holdout traces not used for either training or testing, and found it was able to identify gait-cycle breakdown and freezing events of varying duration. These results suggest that advanced artificial intelligence and automation tools can be developed to enhance the quality, efficiency, and the expansion of wearable sensor data processing capabilities to meet market and industry demand.
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Mikos V, Heng CH, Tay A, Yen SC, Chia NSY, Koh KML, Tan DML, Au WL. A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:503-515. [PMID: 31056518 DOI: 10.1109/tbcas.2019.2914253] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Freezing of Gait (FoG) is a common motor-related impairment among Parkinson's disease patients, which substantially reduces their quality of life and puts them at risk of falls. These patients benefit from wearable FoG detection systems that provide timely biofeedback cues and hence help them regain control over their gait. Unfortunately, the systems proposed thus far are bulky and obtrusive when worn. The objective of this paper is to demonstrate the first integration of an FoG detection system into a single sensor node. To achieve such an integration, features with low computational load are selected and dedicated hardware is designed that limits area and memory utilization. Classification is achieved with a neural network that is capable of learning in real time and thus allows the system to adapt to a patient during run-time. A small form factor FPGA implements the feature extraction and classification, whereas a custom PCB integrates the system into a single node. The system fits into a 4.5 × 3.5 × 1.5 cm 3 housing case, weighs 32 g, and achieves 95.6% sensitivity and 90.2% specificity when adapted to a patient. Biofeedback cues are provided either through auditory or somatosensory means and the system can remain operational for longer than 9 h while providing cues. The proposed system is highly competitive in terms of classification performance and excels with respect to wearability and real-time patient adaptivity.
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Mancini M, Bloem BR, Horak FB, Lewis SJG, Nieuwboer A, Nonnekes J. Clinical and methodological challenges for assessing freezing of gait: Future perspectives. Mov Disord 2019; 34:783-790. [PMID: 31046191 DOI: 10.1002/mds.27709] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/04/2019] [Accepted: 04/11/2019] [Indexed: 01/04/2023] Open
Abstract
Freezing of gait, defined as sudden and usually brief episodes of inability to produce effective stepping, often results in falls and is both disabling and common in parkinsonism. In this narrative review, sprung from the 2nd International Workshop on freezing of gait in Leuven, we summarize the latest insights into clinical and methodological challenges for assessing freezing of gait. We also highlight the role of emerging wearable technology to improve the management of this debilitating symptom. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, The Netherlands
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Simon J G Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Tervuursevest, Belgium
| | - Jorik Nonnekes
- Department of Rehabilitation, Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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Sweeney D, Quinlan LR, Browne P, Richardson M, Meskell P, ÓLaighin G. A Technological Review of Wearable Cueing Devices Addressing Freezing of Gait in Parkinson's Disease. SENSORS 2019; 19:s19061277. [PMID: 30871253 PMCID: PMC6470562 DOI: 10.3390/s19061277] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/01/2019] [Accepted: 03/03/2019] [Indexed: 11/16/2022]
Abstract
Freezing of gait is one of the most debilitating symptoms of Parkinson’s disease and is an important contributor to falls, leading to it being a major cause of hospitalization and nursing home admissions. When the management of freezing episodes cannot be achieved through medication or surgery, non-pharmacological methods such as cueing have received attention in recent years. Novel cueing systems were developed over the last decade and have been evaluated predominantly in laboratory settings. However, to provide benefit to people with Parkinson’s and improve their quality of life, these systems must have the potential to be used at home as a self-administer intervention. This paper aims to provide a technological review of the literature related to wearable cueing systems and it focuses on current auditory, visual and somatosensory cueing systems, which may provide a suitable intervention for use in home-based environments. The paper describes the technical operation and effectiveness of the different cueing systems in overcoming freezing of gait. The “What Works Clearinghouse (WWC)” tool was used to assess the quality of each study described. The paper findings should prove instructive for further researchers looking to enhance the effectiveness of future cueing systems.
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Affiliation(s)
- Dean Sweeney
- Electrical & Electronic Engineering, School of Engineering and Informatics, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Leo R Quinlan
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Physiology, School of Medicine, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Patrick Browne
- Neurology Department, University Hospital Galway, H91 YR71 Galway, Ireland.
- School of Nursing and Midwifery, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- School of Medicine, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Margaret Richardson
- Neurology Department University Hospital Limerick, Dooradoyle, V94 F858 Limerick, Ireland.
| | - Pauline Meskell
- Department of Nursing and Midwifery University of Limerick, Castletroy, V94 T9PX Limerick, Ireland.
| | - Gearóid ÓLaighin
- Electrical & Electronic Engineering, School of Engineering and Informatics, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
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Mazzetta I, Zampogna A, Suppa A, Gumiero A, Pessione M, Irrera F. Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson's Disease Using Electromyography and Inertial Signals. SENSORS 2019; 19:s19040948. [PMID: 30813411 PMCID: PMC6412484 DOI: 10.3390/s19040948] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 02/19/2019] [Accepted: 02/20/2019] [Indexed: 01/13/2023]
Abstract
We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.
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Affiliation(s)
- Ivan Mazzetta
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy.
| | - Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.
- IRCSS NEUROMED Institute, 86077 Pozzilli IS, Italy.
| | | | | | - Fernanda Irrera
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy.
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Punin C, Barzallo B, Clotet R, Bermeo A, Bravo M, Bermeo JP, Llumiguano C. A Non-Invasive Medical Device for Parkinson's Patients with Episodes of Freezing of Gait. SENSORS 2019; 19:s19030737. [PMID: 30759789 PMCID: PMC6387047 DOI: 10.3390/s19030737] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/01/2018] [Accepted: 09/04/2018] [Indexed: 11/17/2022]
Abstract
A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.
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Affiliation(s)
- Catalina Punin
- Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - Boris Barzallo
- Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - Roger Clotet
- Networks and Applied Telematics Group, Universidad Simón Bolívar, Caracas 89000, Venezuela.
| | - Alexander Bermeo
- Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - Marco Bravo
- Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - Juan Pablo Bermeo
- Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - Carlos Llumiguano
- Neurology department, Hospital Vozandes Quito, Quito 170521, Ecuador.
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Dandu SR, Engelhard MM, Qureshi A, Gong J, Lach JC, Brandt-Pearce M, Goldman MD. Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis. IEEE J Biomed Health Inform 2018; 22:40-46. [PMID: 29300700 DOI: 10.1109/jbhi.2017.2773629] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.
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Halilaj E, Rajagopal A, Fiterau M, Hicks JL, Hastie TJ, Delp SL. Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J Biomech 2018; 81:1-11. [PMID: 30279002 DOI: 10.1016/j.jbiomech.2018.09.009] [Citation(s) in RCA: 182] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/08/2018] [Indexed: 12/11/2022]
Abstract
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
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Affiliation(s)
- Eni Halilaj
- Department of Mechanical Engineering, Carnegie Mellon University, United States.
| | - Apoorva Rajagopal
- Department of Mechanical Engineering, Stanford University, United States
| | - Madalina Fiterau
- Department of Computer Science, Stanford University, United States
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, United States
| | - Trevor J Hastie
- Department of Statistics, Stanford University, United States; Department of Health Research and Policy, Stanford University, United States
| | - Scott L Delp
- Department of Mechanical Engineering, Stanford University, United States; Department of Bioengineering, Stanford University, United States; Department of Orthopaedic Surgery, Stanford University, United States
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Complexity of Daily Physical Activity Is More Sensitive Than Conventional Metrics to Assess Functional Change in Younger Older Adults. SENSORS 2018; 18:s18072032. [PMID: 29941835 PMCID: PMC6069067 DOI: 10.3390/s18072032] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 06/19/2018] [Accepted: 06/21/2018] [Indexed: 11/17/2022]
Abstract
The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60–70 years old) participated in a 4-week home-based exercise intervention. The Community Balance and Mobility Scale (CBMS) was used for clinical assessment of the participants’ functional balance and mobility performance pre- and post- intervention. Accelerometers worn on the low back were used to register PA of one week before and in the third week of the intervention. Changes in conventional univariate PA metrics (percentage of walking and sedentary time, step counts, mean cadence) and complexity were compared to the change as measured by the CBMS. Statistical analyses (21 participants) showed significant rank correlation between the change as measured by complexity and CBMS (ρ = 0.47, p = 0.03). Smoothing the activity output improved the correlation (ρ = 0.58, p = 0.01). In contrast, change in univariate PA metrics did not show correlations. These findings demonstrate the high potential of the complexity metric being useful and more sensitive than conventional PA metrics for assessing functional changes in younger older adults.
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45
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Iakovakis D, Hadjidimitriou S, Charisis V, Bostantzopoulou S, Katsarou Z, Hadjileontiadis LJ. Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease. Sci Rep 2018; 8:7663. [PMID: 29769594 PMCID: PMC5955899 DOI: 10.1038/s41598-018-25999-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 05/02/2018] [Indexed: 11/25/2022] Open
Abstract
Parkinson’s disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients’ quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing. Our approach relies on low-/higher-order statistical features of keystrokes timing and pressure variables, computed from short typing sessions. Features are fed into a two-stage multi-model classification pipeline that reaches a decision on the subject’s status (PD patient/control) by gradually fusing prediction probabilities obtained for individual typing sessions and keystroke variables. This method achieved an AUC = 0.92 and 0.82/0.81 sensitivity/specificity (matched groups of 18 early PD patients/15 controls) with discriminant features plausibly correlating with clinical scores of relevant PD motor symptoms. These findings suggest an improvement over similar approaches, thereby constituting a further step towards unobtrusive early PD detection from routine activities.
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Affiliation(s)
- Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Zoe Katsarou
- Department of Neurology, Hippokration Hospital, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece. .,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Samà A, Rodríguez-Martín D, Pérez-López C, Català A, Alcaine S, Mestre B, Prats A, Crespo MC, Bayés À. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2017.05.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Barthel C, Mallia E, Debû B, Bloem BR, Ferraye MU. The Practicalities of Assessing Freezing of Gait. JOURNAL OF PARKINSONS DISEASE 2017; 6:667-674. [PMID: 27662331 PMCID: PMC5088401 DOI: 10.3233/jpd-160927] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background: Freezing of gait (FOG) is a mysterious, complex and debilitating phenomenon in Parkinson’s disease. Adequate assessment is a pre-requisite for managing FOG, as well as for assigning participants in FOG research. The episodic nature of FOG, as well as its multiple clinical expressions make its assessment challenging. Objective: To highlight the available assessment tools and to provide practical, experience-based recommendations for reliable assessment of FOG. Methods: We reviewed FOG assessment from history taking, questionnaires, lab and home-based measurements and examined how these methods account for presence and severity of FOG, their limits and advantages. The practicalities for their use in clinical and research practice are highlighted. Results: According to the available assessment tools severity of FOG is marked by one or a combination of multiple clinical expressions including frequency, duration, triggering circumstances, response to levodopa, association with falls and fear of falling, or need for assistance to avoid falls. Conclusions: To date, a unique methodological tool that encompasses the entire complexity of FOG is lacking. Combining methods should give a better picture of FOG severity, in accordance with the precise clinical or research context. Further development of any future assessment tool requires understanding and thorough analysis of the specific clinical expressions of FOG.
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Affiliation(s)
- Claudia Barthel
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands
| | - Elizabeth Mallia
- Sobell Department for Motor Neuroscience and Movement Disorders, Institute of Neurology, London, UK
| | - Bettina Debû
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, France.,Inserm, Grenoble, France
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands
| | - Murielle Ursulla Ferraye
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands.,Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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Nguyen H, Lebel K, Bogard S, Goubault E, Boissy P, Duval C. Using Inertial Sensors to Automatically Detect and Segment Activities of Daily Living in People With Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2017; 26:197-204. [PMID: 28858808 DOI: 10.1109/tnsre.2017.2745418] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Wearable sensors such as inertial measurement units (IMUs) have been widely used to measure the quantity of physical activities during daily living in healthy and people with movement disorders through activity classification. These sensors have the potential to provide valuable information to evaluate the quality of the movement during the activities of daily living (ADL), such as walking, sitting down, and standing up, which could help clinicians to monitor rehabilitation and pharmaceutical interventions. However, high accuracy in the detection and segmentation of these activities is necessary for proper evaluation of the quality of the performance within a given segment. This paper presents algorithms to process IMU data, to detect and segment unstructured ADL in people with Parkinson's disease (PD) in simulated free-living environment. The proposed method enabled the detection of 1610 events of ADL performed by nine community dwelling older adults with PD under simulated free-living environment with 90% accuracy (sensitivity = 90.8%, specificity = 97.8%) while segmenting these activities within 350 ms of the "gold-standard" manual segmentation. These results demonstrate the robustness of the proposed method to eventually be used to automatically detect and segment ADL in free-living environment in people with PD. This could potentially lead to a more expeditious evaluation of the quality of the movement and administration of proper corrective care for patients who are under physical rehabilitation and pharmaceutical intervention for movement disorders.
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Suppa A, Kita A, Leodori G, Zampogna A, Nicolini E, Lorenzi P, Rao R, Irrera F. l-DOPA and Freezing of Gait in Parkinson's Disease: Objective Assessment through a Wearable Wireless System. Front Neurol 2017; 8:406. [PMID: 28855889 PMCID: PMC5557738 DOI: 10.3389/fneur.2017.00406] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 07/28/2017] [Indexed: 01/21/2023] Open
Abstract
Freezing of gait (FOG) is a leading cause of falls and fractures in Parkinson’s disease (PD). The episodic and rather unpredictable occurrence of FOG, coupled with the variable response to l-DOPA of this gait disorder, makes the objective evaluation of FOG severity a major clinical challenge in the therapeutic management of patients with PD. The aim of this study was to examine and compare gait, clinically and objectively, in patients with PD, with and without FOG, by means of a new wearable system. We also assessed the effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters in patients with and without FOG. To this purpose, we recruited 28 patients with FOG, 16 patients without FOG, and 16 healthy subjects. In all participants, gait was evaluated clinically by video recordings and objectively by means of the wearable wireless system, during a modified 3-m Timed Up and Go (TUG) test. All patients performed the modified TUG test under and not under dopaminergic therapy (ON and OFF therapy). By comparing instrumental data with the clinical identification of FOG based on offline video-recordings, we also assessed the performance of the wearable system to detect FOG automatically in terms of sensitivity, specificity, positive and negative predictive values, and finally accuracy. TUG duration was longer in patients than in controls, and the amount of gait abnormalities was prominent in patients with FOG compared with those without FOG. l-DOPA improved gait significantly in patients with PD and particularly in patients with FOG mainly by reducing FOG duration and increasing specific spatiotemporal gait parameters. Finally, the overall wireless system performance in automatic FOG detection was characterized by excellent sensitivity (93.41%), specificity (98.51%), positive predictive value (89.55%), negative predictive value (97.31%), and finally accuracy (98.51%). Our study overall provides new information on the beneficial effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters as objectively measured by a wearable sensory system. The algorithm here reported potentially opens to objective long-time sensing of FOG episodes in patients with PD.
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Affiliation(s)
- Antonio Suppa
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy.,IRCCS Neuromed Institute, Pozzilli, Italy
| | - Ardian Kita
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, Italy
| | - Giorgio Leodori
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Alessandro Zampogna
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Ettore Nicolini
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Paolo Lorenzi
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, Italy
| | - Rosario Rao
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, Italy
| | - Fernanda Irrera
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, Italy
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Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications. PARKINSONS DISEASE 2017; 2017:6139716. [PMID: 28607801 PMCID: PMC5451764 DOI: 10.1155/2017/6139716] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 03/23/2017] [Accepted: 04/27/2017] [Indexed: 11/17/2022]
Abstract
Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.
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