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Elbatanouny H, Kleanthous N, Dahrouj H, Alusi S, Almajali E, Mahmoud S, Hussain A. Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3959. [PMID: 38931743 PMCID: PMC11207947 DOI: 10.3390/s24123959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
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Affiliation(s)
- Hagar Elbatanouny
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | | | - Hayssam Dahrouj
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Sundus Alusi
- The Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK;
| | - Eqab Almajali
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Soliman Mahmoud
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
- University of Khorfakkan, Khorfakkan, Sharjah 18119, United Arab Emirates
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
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Marom P, Brik M, Agay N, Dankner R, Katzir Z, Keshet N, Doron D. The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability. SENSORS (BASEL, SWITZERLAND) 2024; 24:3594. [PMID: 38894386 PMCID: PMC11175355 DOI: 10.3390/s24113594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.
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Affiliation(s)
- Pnina Marom
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Department of Health Promotion, School of Public Health, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Michael Brik
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
| | - Nirit Agay
- Unit for Cardiovascular Epidemiology, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Ramat Gan 5262000, Israel;
| | - Rachel Dankner
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Unit for Cardiovascular Epidemiology, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Ramat Gan 5262000, Israel;
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Zoya Katzir
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Department of General Medicine, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Naama Keshet
- Department of Physical Therapy, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel;
| | - Dana Doron
- Ambulatory Day Care, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel
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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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Scully AE, Neo K, Lim E, Manharlal PK, de Oliveira B, Hill KD, Clark R, Pua YH, Tan D. Reliability and variability of physiotherapists scoring freezing of gait through video analysis. Physiother Theory Pract 2023:1-11. [PMID: 37639503 DOI: 10.1080/09593985.2023.2252059] [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: 05/27/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND The "gold standard" marker for freezing of gait severity is percentage of time spent with freezing observed through video analysis. OBJECTIVE This study examined inter- and intra-rater reliability and variability of physiotherapists rating freezing of gait severity through video analysis and explored the effects of experience. METHODS Thirty physiotherapists rated 14 videos of Timed Up and Go performance by people with Parkinson's and gait freezing. Ten videos were unique, while four were repeated. Freezing frequency, total duration, and percentage of time spent with freezing were computed. Reliability and variability were estimated using ICC (2,1) and mean absolute differences. Between-group differences were calculated with the one-way ANOVA. RESULTS Inter- and intra-rater reliability ranged from moderate to good (ICC: inter-rater frequency = 0.63, duration = 0.78, percentage = 0.50; intra-rater frequency = 0.84, duration = 0.89, percentage = 0.50). Variability for freezing frequency was two episodes. Inter- and intra-rater variability for total freezing duration was 18.8 and 12.3 seconds, respectively. For percentage of time spent with freezing, this was 15.2% and 13.5%. Physiotherapy experience had no effect. CONCLUSION Physiotherapists demonstrated sufficient reliability, but variability was large enough to cause changes in severity classifications on existing rating scales. Percentage of time spent with freezing was the least reliable marker, supporting the use of freezing frequency or total duration instead.
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Affiliation(s)
- Aileen E Scully
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Kenneth Neo
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Eunice Lim
- Health and Social Sciences, Singapore Institute of Technology, Singapore
| | - Prakash K Manharlal
- Department of Neurology (SGH Campus), National Neuroscience Institute @Singapore General Hospital, Singapore
| | - Beatriz de Oliveira
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
| | - Keith D Hill
- Rehabilitation, Ageing and Independent Living (RAIL) Research Centre, Monash University, Frankston, Victoria, Australia
| | - Ross Clark
- School of Health, University of the Sunshine Coast, Queensland, Australia
| | - Yong Hao Pua
- Department of Physiotherapy, Singapore General Hospital, Singapore
| | - Dawn Tan
- Health and Social Sciences, Singapore Institute of Technology, Singapore
- Department of Physiotherapy, Singapore General Hospital, Singapore
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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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Cockx H, Nonnekes J, Bloem B, van Wezel R, Cameron I, Wang Y. Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection? J Neuroeng Rehabil 2023; 20:53. [PMID: 37106388 PMCID: PMC10134593 DOI: 10.1186/s12984-023-01175-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Freezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson's disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling/shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection. METHODS Sixteen people with Parkinson's disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 378 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures. RESULTS The FI increased significantly during trembling and akinetic FOG, but increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events. CONCLUSION When the power in the locomotion band (0.5-3 Hz) decreases, the FI increases and is unable to specify whether a stop is voluntary or involuntary (i.e. trembling or akinetic FOG). In contrast, the heart rate can reveal whether there is the intention to move, thus distinguishing FOG from stopping. We suggest that the combination of a motion sensor and a heart rate monitor may be promising for future FOG detection.
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Affiliation(s)
- Helena Cockx
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands.
| | - Jorik Nonnekes
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, The Netherlands
| | - Bastiaan Bloem
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Richard van Wezel
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
| | - Ian Cameron
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
- OnePlanet Research Center, Nijmegen, The Netherlands
| | - Ying Wang
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
- ZGT Academy, Ziekenhuisgroep Twente, Almelo, The Netherlands
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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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Amprimo G, Masi G, Priano L, Azzaro C, Galli F, Pettiti G, Mauro A, Ferraris C. Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect. SENSORS (BASEL, SWITZERLAND) 2022; 22:8173. [PMID: 36365870 PMCID: PMC9654712 DOI: 10.3390/s22218173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/17/2022] [Accepted: 10/22/2022] [Indexed: 06/16/2023]
Abstract
Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.
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Affiliation(s)
- Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giulia Masi
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Lorenzo Priano
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Corrado Azzaro
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Federica Galli
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Alessandro Mauro
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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10
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Li KP, Zhang ZQ, Zhou ZL, Su JQ, Wu XH, Shi BH, Xu JG. Effect of music-based movement therapy on the freezing of gait in patients with Parkinson’s disease: A randomized controlled trial. Front Aging Neurosci 2022; 14:924784. [PMID: 36337701 PMCID: PMC9627030 DOI: 10.3389/fnagi.2022.924784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022] Open
Abstract
Background Progression of freezing of gait (FOG), a common pathological gait in Parkinson’s disease (PD), has been shown to be an important risk factor for falls, loss of independent living ability, and reduced quality of life. However, previous evidence indicated poor efficacy of medicine and surgery in treating FOG in patients with PD. Music-based movement therapy (MMT), which entails listening to music while exercising, has been proposed as a treatment to improve patients’ motor function, emotions, and physiological activity. In recent years, MMT has been widely used to treat movement disorders in neurological diseases with promising results. Results from our earlier pilot study revealed that MMT could relieve FOG and improve the quality of life for patients with PD. Objective To explore the effect of MMT on FOG in patients with PD. Materials and methods This was a prospective, evaluator-blinded, randomized controlled study. A total of 81 participants were randomly divided into music-based movement therapy group (MMT, n = 27), exercise therapy group (ET, n = 27), and control group (n = 27). Participants in the MMT group were treated with MMT five times (1 h at a time) every week for 4 weeks. Subjects in the ET group were intervened in the same way as the MMT group, but without music. Routine rehabilitation treatment was performed on participants in all groups. The primary outcome was the change of FOG in patients with PD. Secondary evaluation indicators included FOG-Questionnaire (FOG-Q) and the comprehensive motor function. Results After 4 weeks of intervention, the double support time, the cadence, the max flexion of knee in stance, the max hip extension, the flexion moment of knee in stance, the comprehensive motor function (UPDRS Part III gait-related items total score, arising from chair, freezing of gait, postural stability, posture, MDS-UPDRS Part II gait-related items total score, getting out of bed/a car/deep chair, walking and balance, freezing), and the FOG-Q in the MMT group were lower than that in the control group and ET group (p < 0.05). The gait velocity, the max ankle dorsiflexion in stance, ankle range of motion (ROM) during push-off, ankle ROM over gait cycle, the knee ROM over gait cycle, and the max extensor moment in stance (ankle, knee) in the MMT group were higher than that in the control group and ET group (p < 0.05). However, no significant difference was reported between the control group and ET group (p > 0.05). The stride length and hip ROM over gait cycle in the MMT group were higher than that in the control group (p < 0.05), and the max knee extension in stance in the MMT group was lower than that in the control group (p < 0.05). Nevertheless, there was no significant difference between the ET group and MMT group (p > 0.05) or control group (p > 0.05). Conclusion MMT improved gait disorders in PD patients with FOG, thereby improving their comprehensive motor function.
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Affiliation(s)
- Kun-peng Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zeng-qiao Zhang
- Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zong-lei Zhou
- School of Public Health, Fudan University, Shanghai, China
| | - Jian-qing Su
- Department of Neurorehabilitation, The Second Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Xian-hua Wu
- Changqiao Community Health Service Centre, Shanghai, China
| | - Bo-han Shi
- Department of Neurorehabilitation, The Second Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Jian-guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Ministry of Education, Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Shanghai, China
- *Correspondence: Jian-guang Xu,
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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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12
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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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13
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A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease. SENSORS 2022; 22:s22072613. [PMID: 35408226 PMCID: PMC9002774 DOI: 10.3390/s22072613] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 02/05/2023]
Abstract
Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.
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14
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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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15
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Pardoel S, Shalin G, Lemaire ED, Kofman J, Nantel J. Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease. PLoS One 2021; 16:e0258544. [PMID: 34637473 PMCID: PMC8509886 DOI: 10.1371/journal.pone.0258544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 09/29/2021] [Indexed: 11/24/2022] Open
Abstract
Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.
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Affiliation(s)
- Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Gaurav Shalin
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Edward D. Lemaire
- Faculty of Medicine, University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada
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16
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Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 2021; 45:87. [PMID: 34392429 PMCID: PMC8364438 DOI: 10.1007/s10916-021-01760-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/04/2021] [Indexed: 01/21/2023]
Abstract
Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.
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Affiliation(s)
- Libak Abou
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joseph Peters
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ellyce Wong
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca Akers
- Department of Rehabilitation Science, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mauricette Sènan Dossou
- Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, Medical Center, University of Kansas, Kansas City, KS, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. MED 2021; 2:642-665. [DOI: 10.1016/j.medj.2021.04.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 03/22/2021] [Accepted: 04/06/2021] [Indexed: 12/24/2022]
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18
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Guillén-Rogel P, Barbado D, Franco-Escudero C, San Emeterio C, Marín PJ. Are Core Stability Tests Related to Single Leg Squat Performance in Active Females? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115548. [PMID: 34067492 PMCID: PMC8196943 DOI: 10.3390/ijerph18115548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 12/02/2022]
Abstract
Core stability (CS) deficits can have a significant impact on lower limb function. The aim of this study was to investigate the relationship between two dynamic core exercise assessments and dynamic knee valgus during single-leg squats. In total, 20 physically active female students participated in this study. The OCTOcore smartphone application assesses CS during two dynamic exercise tests, the partial range single-leg deadlift (SLD) test and the bird-dog (BD) test. A two-dimensional assessment of a single-leg squat test was used to quantify participants’ hip frontal angle (HFASLS) and knee frontal plane projection angle (FPPASLS). Ankle dorsiflexion was evaluated through the weight-bearing dorsiflexion test. The correlational analyses indicated that the HFASLS was significantly related to the partial range single-leg deadlift test (r = 0.314, p < 0.05) and ankle dorsiflexion (r = 0.322, p < 0.05). The results showed a significant difference (p < 0.05) in the CS test between cases categorised as dynamic knee valgus (>10°) and normal (≤10°). The CS deficit may influence the neuromuscular control of the lumbopelvic-hip complex during single-leg movements. The link between CS and kinematic factors related to knee injuries was only observed when CS was measured in the SLD test but not in the BD test.
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Affiliation(s)
- Paloma Guillén-Rogel
- Laboratory of Physiology, Faculty of Health Sciences, Miguel de Cervantes European University, 47012 Valladolid, Spain; (P.G.-R.); (C.F.-E.); (C.S.E.)
| | - David Barbado
- Sport Research Centre, Miguel Hernández University, 03202 Elche, Spain;
| | - Cristina Franco-Escudero
- Laboratory of Physiology, Faculty of Health Sciences, Miguel de Cervantes European University, 47012 Valladolid, Spain; (P.G.-R.); (C.F.-E.); (C.S.E.)
| | - Cristina San Emeterio
- Laboratory of Physiology, Faculty of Health Sciences, Miguel de Cervantes European University, 47012 Valladolid, Spain; (P.G.-R.); (C.F.-E.); (C.S.E.)
| | - Pedro J. Marín
- Development Research, CYMO Research Institute, 47140 Valladolid, Spain
- Correspondence:
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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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20
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Borzì L, Mazzetta I, Zampogna A, Suppa A, Olmo G, Irrera F. Prediction of Freezing of Gait in Parkinson's Disease Using Wearables and Machine Learning. SENSORS 2021; 21:s21020614. [PMID: 33477323 PMCID: PMC7830634 DOI: 10.3390/s21020614] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/07/2021] [Accepted: 01/13/2021] [Indexed: 01/06/2023]
Abstract
Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.
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Affiliation(s)
- Luigi Borzì
- Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy;
- Correspondence:
| | - Ivan Mazzetta
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy; (I.M.); (F.I.)
| | - Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (A.S.)
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (A.S.)
- IRCCS NEUROMED Institute, 86077 Pozzilli, Italy
| | - Gabriella Olmo
- Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy;
| | - Fernanda Irrera
- Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy; (I.M.); (F.I.)
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21
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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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22
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Deep Brain Stimulation Selection Criteria for Parkinson's Disease: Time to Go beyond CAPSIT-PD. J Clin Med 2020; 9:jcm9123931. [PMID: 33291579 PMCID: PMC7761824 DOI: 10.3390/jcm9123931] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 11/24/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
Despite being introduced in clinical practice more than 20 years ago, selection criteria for deep brain stimulation (DBS) in Parkinson's disease (PD) rely on a document published in 1999 called 'Core Assessment Program for Surgical Interventional Therapies in Parkinson's Disease'. These criteria are useful in supporting the selection of candidates. However, they are both restrictive and out-of-date, because the knowledge on PD progression and phenotyping has massively evolved. Advances in understanding the heterogeneity of PD presentation, courses, phenotypes, and genotypes, render a better identification of good DBS outcome predictors a research priority. Additionally, DBS invasiveness, cost, and the possibility of serious adverse events make it mandatory to predict as accurately as possible the clinical outcome when informing the patients about their suitability for surgery. In this viewpoint, we analyzed the pre-surgical assessment according to the following topics: early versus delayed DBS; the evolution of the levodopa challenge test; and the relevance of axial symptoms; patient-centered outcome measures; non-motor symptoms; and genetics. Based on the literature, we encourage rethinking of the selection process for DBS in PD, which should move toward a broad clinical and instrumental assessment of non-motor symptoms, quantitative measurement of gait, posture, and balance, and in-depth genotypic and phenotypic characterization.
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Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, Ríos-Urrego CD, Strauss M, Carvajal-Castaño HA, Bayerl S, Castrillón-Osorio LR, Arias-Vergara T, Künderle A, López-Pabón FO, Parra-Gallego LF, Eskofier B, Gómez-Gómez LF, Schuster M, Nöth E. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag 2020; 10:137-157. [PMID: 32571150 DOI: 10.2217/nmt-2019-0037] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson's disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.
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Affiliation(s)
- Juan Rafael Orozco-Arroyave
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia.,LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
| | - Juan Camilo Vásquez-Correa
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia.,LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
| | - Philipp Klumpp
- LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
| | - Paula Andrea Pérez-Toro
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia
| | - Daniel Escobar-Grisales
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia
| | - Nils Roth
- MaD Lab, Faculty of Engineering, University or Erlangen, 91052 Erlangen-Germany
| | | | - Martin Strauss
- LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
| | | | - Sebastian Bayerl
- LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
| | | | - Tomas Arias-Vergara
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia.,LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany.,Department of Otorhinolaryngology, Head & Neck Surgery, Ludwig-Maximilians University, 81377 Munich-Germany
| | - Arne Künderle
- MaD Lab, Faculty of Engineering, University or Erlangen, 91052 Erlangen-Germany
| | | | | | - Björn Eskofier
- MaD Lab, Faculty of Engineering, University or Erlangen, 91052 Erlangen-Germany
| | - Luis Felipe Gómez-Gómez
- GITA Research Lab, Faculty of Engineering, University of Antioquia, 050010 Medellín-Colombia
| | - Maria Schuster
- Department of Otorhinolaryngology, Head & Neck Surgery, Ludwig-Maximilians University, 81377 Munich-Germany
| | - Elmar Nöth
- LME Lab, Faculty of Engineering, University of Erlangen, 91058 Erlangen-Germany
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Tahafchi P, Judy JW. Freezing-of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4246-4249. [PMID: 31946806 DOI: 10.1109/embc.2019.8856480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Freezing of Gait (FoG) is an episodic motor disturbance in Parkinson disease (PD) that causes patients to be unable to initiate or maintain their locomotion. Prior work that used simple and easy-to-learn algorithms based on a singular feature and rule-based classifiers are not sufficient to learn variations in patient walking styles and freezing patterns. Efforts to use machine-learning algorithms suffer from challenges caused by imbalanced datasets. Here, we propose a new approach for FoG detection using a wide set of online calculable features and an instance-based and non-parametric Possibilistic K-Nearest-Neighbor (KNN) classifier. The issue of imbalanced datasets is addressed using the Self-Organizing-Map (SOM) algorithm.
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25
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Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, Mercuri NB, Saggio G, Pisani A. Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:113-122. [PMID: 31594252 DOI: 10.3233/jpd-191758] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases. OBJECTIVE To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)]. METHODS We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDS-UPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal- Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time. RESULTS Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and Pull Test. CONCLUSIONS In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.
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Affiliation(s)
- Giulia Di Lazzaro
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Mariachiara Ricci
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Mohammad Al-Wardat
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Tommaso Schirinzi
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Simona Scalise
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Franco Giannini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Nicola B Mercuri
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- Santa Lucia Foundation, IRCCS, Rome, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Antonio Pisani
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- Santa Lucia Foundation, IRCCS, Rome, Italy
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26
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WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning. ELECTRONICS 2019. [DOI: 10.3390/electronics8121433] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson’s Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time–frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.
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27
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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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Capecci M, Pournajaf S, Galafate D, Sale P, Le Pera D, Goffredo M, De Pandis MF, Andrenelli E, Pennacchioni M, Ceravolo MG, Franceschini M. Clinical effects of robot-assisted gait training and treadmill training for Parkinson's disease. A randomized controlled trial. Ann Phys Rehabil Med 2019; 62:303-312. [DOI: 10.1016/j.rehab.2019.06.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 06/28/2019] [Accepted: 06/30/2019] [Indexed: 11/25/2022]
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29
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Müller MLTM, Marusic U, van Emde Boas M, Weiss D, Bohnen NI. Treatment options for postural instability and gait difficulties in Parkinson's disease. Expert Rev Neurother 2019; 19:1229-1251. [PMID: 31418599 DOI: 10.1080/14737175.2019.1656067] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Introduction: Gait and balance disorders in Parkinson's disease (PD) represent a major therapeutic challenge as frequent falls and freezing of gait impair quality of life and predict mortality. Limited dopaminergic therapy responses implicate non-dopaminergic mechanisms calling for alternative therapies.Areas covered: The authors provide a review that encompasses pathophysiological changes involved in axial motor impairments in PD, pharmacological approaches, exercise, and physical therapy, improving physical activity levels, invasive and non-invasive neurostimulation, cueing interventions and wearable technology, and cognitive interventions.Expert opinion: There are many promising therapies available that, to a variable degree, affect gait and balance disorders in PD. However, not one therapy is the 'silver bullet' that provides full relief and ultimately meaningfully improves the patient's quality of life. Sedentariness, apathy, and emergence of frailty in advancing PD, especially in the setting of medical comorbidities, are perhaps the biggest threats to experience sustained benefits with any of the available therapeutic options and therefore need to be aggressively treated as early as possible. Multimodal or combination therapies may provide complementary benefits to manage axial motor features in PD, but selection of treatment modalities should be tailored to the individual patient's needs.
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Affiliation(s)
- Martijn L T M Müller
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre of Koper, Koper, Slovenia.,Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Miriam van Emde Boas
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel Weiss
- Centre for Neurology, Department for Neurodegenerative Diseases and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Nicolaas I Bohnen
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA.,Geriatric Research Education and Clinical Center, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan, Ann Arbor, USA
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Guillén-Rogel P, Franco-Escudero C, Marín PJ. Test-retest reliability of a smartphone app for measuring core stability for two dynamic exercises. PeerJ 2019; 7:e7485. [PMID: 31413933 PMCID: PMC6690332 DOI: 10.7717/peerj.7485] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/16/2019] [Indexed: 01/12/2023] Open
Abstract
Background Recently, there has been growing interest in using smartphone applications to assess gait speed and quantify isometric core stability exercise intensity. The purpose of this study was to investigate the between-session reliability and minimal detectable change of a smartphone app for two dynamic exercise tests of the lumbopelvic complex. Methods Thirty-three healthy young and active students (age: 22.3 ± 5.9 years, body weight: 66.9 ± 11.3 kg, height: 167.8 ± 10.3 cm) participated in this study. Intraclass correlation coefficient (ICC), coefficient of variation (%CV), and Bland–Altman plots were used to verify the reliability of the test. The standard error of measurement (SEM) and the minimum detectable difference (MDD) were calculated for clinical applicability. Results The ICCs ranged from 0.73 to 0.96, with low variation (0.9% to 4.8%) between days of assessments. The Bland–Altman plots and one-sample t-tests (p > 0.05) indicated that no dynamic exercise tests changed systematically. Our analyses showed that SEM 0.6 to 1.5 mm/s-2) and MDD (2.1 to 3.5 mm/s-2). Conclusion The OCTOcore app is a reliable tool to assess core stability for two dynamic exercises. A minimal change of 3.5 mm/s-2 is needed to be confident that the change is not a measurement error between two sessions.
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Affiliation(s)
- Paloma Guillén-Rogel
- Laboratory of Physiology, European University Miguel de Cervantes, Valladolid, Spain
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31
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The CuePed Trial: How Does Environmental Complexity Impact Cue Effectiveness? A Comparison of Tonic and Phasic Visual Cueing in Simple and Complex Environments in a Parkinson's Disease Population with Freezing of Gait. PARKINSONS DISEASE 2019; 2019:2478980. [PMID: 31428302 PMCID: PMC6681574 DOI: 10.1155/2019/2478980] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/23/2019] [Accepted: 07/08/2019] [Indexed: 01/23/2023]
Abstract
Background The optimal prescription of cueing for the treatment of freezing of gait (FoG) in Parkinson's disease (PD) is currently a difficult problem for clinicians due to the heterogeneity of cueing modalities, devices, and the limited comparative trial evidence. There has been a rise in the development of motion-sensitive, wearable cueing devices for the treatment of FoG in PD. These devices generally produce cues after signature gait or electroencephalographic antecedents of FoG episodes are detected (phasic cues). It is not known whether these devices offer benefit over simple (tonic) cueing devices. Methods We assembled 20 participants with PD and FoG and familiarized them with a belt-worn, laser-light cueing device (Agilitas™). The device was designed with 2 cueing modalities—gait-dependent or “phasic” cueing and gait-independent or “tonic” cueing. Participants used the device sequentially in the off, phasic, or tonic modes, across 2 tasks—a 2-minute walk and an obstacle course. Results A significant improvement in mean distance walked during the 2-minute walk test was observed for the tonic mode (127.3 m) compared with the off (111.4 m) and phasic (116.1 m) conditions. In contrast, there was a nonsignificant trend toward improvement in FoG frequency, duration, and course time when the device was switched from off to tonic and to phasic modes for the obstacle course. Conclusions Parkinson's disease patients with FoG demonstrated an improvement in distance walked during the two-minute walk test when a cueing device was switched from off to phasic and to tonic modes of operation. However, this benefit was lost when patients negotiated an obstacle course.
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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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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: 86] [Impact Index Per Article: 17.2] [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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Mirelman A, Bonato P, Camicioli R, Ellis TD, Giladi N, Hamilton JL, Hass CJ, Hausdorff JM, Pelosin E, Almeida QJ. Gait impairments in Parkinson's disease. Lancet Neurol 2019; 18:697-708. [PMID: 30975519 DOI: 10.1016/s1474-4422(19)30044-4] [Citation(s) in RCA: 312] [Impact Index Per Article: 62.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 01/16/2019] [Accepted: 01/23/2019] [Indexed: 12/19/2022]
Abstract
Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | | | - Terry D Ellis
- Department of Physical Therapy and Athletic Training, Boston University, Boston, MA, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jamie L Hamilton
- Michael J Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Chris J Hass
- College of Health and Human Performance, Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Elisa Pelosin
- Department of Neuroscience (DINOGMI), University of Genova, Genova, Italy; IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Quincy J Almeida
- Movement Disorders Research and Rehabilitation Centre, Wilfrid Laurier University, Waterloo, ON, Canada
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Ali SA, Kovatch KJ, Hwang C, Bohm LA, Zopf DA, Thorne MC. Assessment of Application-Driven Postoperative Care in the Pediatric Tonsillectomy Population: A Survey-Based Pilot Study. JAMA Otolaryngol Head Neck Surg 2019; 145:285-287. [PMID: 30730540 DOI: 10.1001/jamaoto.2018.3647] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- S Ahmed Ali
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
| | - Kevin J Kovatch
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
| | - Charles Hwang
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
| | - Lauren A Bohm
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
| | - David A Zopf
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
| | - Marc C Thorne
- Department of Otolaryngology-Head & Neck Surgery, Michigan Medicine, Ann Arbor, Michigan
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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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Disease Activity Patterns Recorded Using a Mobile Monitoring System Are Associated with Clinical Outcomes of Patients with Crohn's Disease. Dig Dis Sci 2018; 63:2220-2230. [PMID: 29779084 DOI: 10.1007/s10620-018-5110-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 05/04/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND Usefulness of a mobile monitoring system for Crohn's disease (CD) has not been evaluated. We aimed to determine whether disease activity patterns depicted using a web-based symptom diary for CD could indicate disease clinical outcomes. METHODS Patients with CD from tertiary hospitals were prospectively invited to record their symptoms using a smartphone at least once a week. Disease activity patterns for at least 2 months were statistically classified into good and poor groups based on two factors in two consecutive time frames; the degree of score variation (maximum-minimum) in each frame and the trend (upward, stationary, or downward) of patterns indicated by the difference in the mean activity scores between two time frames. RESULTS Overall, 220 (82.7%) and 46 (17.3%) patients were included in good and poor groups, respectively. Poor group was significantly more associated with disease-related hospitalization (p = 0.004), unscheduled hospital visits (p = 0.005), and bowel surgery (p < 0.001) during the follow-up period than good group. In the multivariate analysis, poor patterns [odds ratio (OR) 2.62, p = 0.006], stricturing (OR 4.19, p < 0.001) or penetrating behavior (OR 2.27, p = 0.012), and young age at diagnosis (OR 1.06, p = 0.019) were independently associated with disease-related hospitalization. Poor patterns (OR 4.06, p = 0.006) and an ileal location (OR 5.79, p = 0.032) remained independent risk factors for unscheduled visits. Poor patterns (OR 15.2, p < 0.001) and stricturing behavior (OR 9.77, p = 0.004) were independent risk factors for bowel surgery. CONCLUSION The disease activity patterns depicted using a web-based symptom diary were useful indicators of poor clinical outcomes in patients with CD.
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Reinertsen E, Clifford GD. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas 2018; 39:05TR01. [PMID: 29671754 PMCID: PMC5995114 DOI: 10.1088/1361-6579/aabf64] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.
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Affiliation(s)
- Erik Reinertsen
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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VanWye WR, Hoover DL. Management of a patient's gait abnormality using smartphone technology in-clinic for improved qualitative analysis: A case report. Physiother Theory Pract 2018; 34:403-410. [PMID: 29308956 DOI: 10.1080/09593985.2017.1419326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND AND PURPOSE Qualitative analysis has its limitations as the speed of human movement often occurs more quickly than can be comprehended. Digital video allows for frame-by-frame analysis, and therefore likely more effective interventions for gait dysfunction. Although the use of digital video outside laboratory settings, just a decade ago, was challenging due to cost and time constraints, rapid use of smartphones and software applications has made this technology much more practical for clinical usage. CASE DESCRIPTION A 35-year-old man presented for evaluation with the chief complaint of knee pain 24 months status-post triple arthrodesis following a work-related crush injury. In-clinic qualitative gait analysis revealed gait dysfunction, which was augmented by using a standard IPhone® 3GS camera. After video capture, an IPhone® application (Speed Up TV®, https://itunes.apple.com/us/app/speeduptv/id386986953?mt=8 ) allowed for frame-by-frame analysis. Corrective techniques were employed using in-clinic equipment to develop and apply a temporary heel-to-toe rocker sole (HTRS) to the patient's shoe. OUTCOMES Post-intervention video revealed significantly improved gait efficiency with a decrease in pain. The patient was promptly fitted with a permanent HTRS orthosis. This intervention enabled the patient to successfully complete a work conditioning program and progress to job retraining. DISCUSSION Video allows for multiple views, which can be further enhanced by using applications for frame-by-frame analysis and zoom capabilities. This is especially useful for less experienced observers of human motion, as well as for establishing comparative signs prior to implementation of training and/or permanent devices.
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Affiliation(s)
- William R VanWye
- a Department of Physical Therapy , Western Kentucky University , Bowling Green , USA
| | - Donald L Hoover
- b Department of Physical Therapy , Western Michigan University , Kalamazoo , MI
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Manor B, Yu W, Zhu H, Harrison R, Lo OY, Lipsitz L, Travison T, Pascual-Leone A, Zhou J. Smartphone App-Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability. JMIR Mhealth Uhealth 2018; 6:e36. [PMID: 29382625 PMCID: PMC5811655 DOI: 10.2196/mhealth.8815] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/24/2017] [Accepted: 11/24/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Walking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and "dual-task" walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings. OBJECTIVE The objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings. METHODS We created an iPhone app that used the phone's motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user's pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard-instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times-a clinically meaningful metric of locomotor control-from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time (ie, percentage change from normal to dual-task conditions) from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses. RESULTS Across all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (P<.001, r2=.98). These correlations were independent of walking condition and pocket tightness. App- and GAITRite-derived stride-time dual-task costs were also highly correlated (P<.001, r2=.95). The error of app-derived stride times (mean 16.9, SD 9.0 ms) was unaffected by the magnitude of stride time, walking condition, or pocket tightness. For both normal and dual-task trials, average stride times derived from app walking trials demonstrated excellent test-retest reliability within and between both laboratory and home-based assessments (intraclass correlation coefficient range .82-.94). CONCLUSIONS The iPhone app we created enabled valid and reliable assessment of stride timing-with the smartphone in the pocket-during both normal and dual-task walking and within both laboratory and nonlaboratory environments. Additional work is warranted to expand the functionality of this tool to older adults and other patient populations.
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Affiliation(s)
- Brad Manor
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Wanting Yu
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Hao Zhu
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Rachel Harrison
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - On-Yee Lo
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Lewis Lipsitz
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Thomas Travison
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Interventional Cognitive Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Junhong Zhou
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Roslindale, MA, United States
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Abstract
Gait is one of the keys to functional independence. For a long-time, walking was considered an automatic process involving minimal higher-level cognitive input. Indeed, walking does not take place without muscles that move the limbs and the "lower-level" control that regulates the timely activation of the muscles. However, a growing body of literature suggests that walking can be viewed as a cognitive process that requires "higher-level" cognitive control, especially during challenging walking conditions that require executive function and attention. Two main locomotor pathways have been identified involving multiple brain areas for the control of posture and gait: the dorsal pathway of cognitive locomotor control and the ventral pathway for emotional locomotor control. These pathways may be distinctly affected in different pathologies that have important implications for rehabilitation and therapy. The clinical assessment of gait should be a focused, simple, and cost-effective process that provides both quantifiable and qualitative information on performance. In the last two decades, gait analysis has gradually shifted from analysis of a few steps in a restricted space to long-term monitoring of gait using body fixed sensors, capturing real-life and routine behavior in the home and community environment. The chapter also describes this evolution and its implications.
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Affiliation(s)
- Anat Mirelman
- Center for the Study of Movement, Cognition, and Mobility, Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Laboratory of Early Markers of Neurodegeneration, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Shirley Shema
- Center for the Study of Movement, Cognition, and Mobility, Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbal Maidan
- Center for the Study of Movement, Cognition, and Mobility, Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Laboratory of Early Markers of Neurodegeneration, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jeffery M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel; Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Israel; Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States.
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Rovini E, Maremmani C, Cavallo F. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review. Front Neurosci 2017; 11:555. [PMID: 29056899 PMCID: PMC5635326 DOI: 10.3389/fnins.2017.00555] [Citation(s) in RCA: 192] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/21/2017] [Indexed: 01/15/2023] Open
Abstract
Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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Affiliation(s)
- Erika Rovini
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Carlo Maremmani
- U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa, Italy
| | - Filippo Cavallo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
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Pepa L, Verdini F, Spalazzi L. Gait parameter and event estimation using smartphones. Gait Posture 2017; 57:217-223. [PMID: 28667903 DOI: 10.1016/j.gaitpost.2017.06.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 06/13/2017] [Accepted: 06/15/2017] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well. METHODS This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects. RESULTS High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count. CONCLUSION This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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Affiliation(s)
- Lucia Pepa
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
| | - Federica Verdini
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
| | - Luca Spalazzi
- Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.
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Schneider RB, Biglan KM. The promise of telemedicine for chronic neurological disorders: the example of Parkinson's disease. Lancet Neurol 2017; 16:541-551. [DOI: 10.1016/s1474-4422(17)30167-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 04/02/2017] [Accepted: 05/03/2017] [Indexed: 10/19/2022]
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Silva de Lima AL, Evers LJW, Hahn T, Bataille L, Hamilton JL, Little MA, Okuma Y, Bloem BR, Faber MJ. Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review. J Neurol 2017; 264:1642-1654. [PMID: 28251357 PMCID: PMC5533840 DOI: 10.1007/s00415-017-8424-0] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 02/15/2017] [Accepted: 02/16/2017] [Indexed: 12/18/2022]
Abstract
Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.
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Affiliation(s)
- Ana Lígia Silva de Lima
- Radboud university medical center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands. .,Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands. .,CAPES Foundation, Ministry of Education of Brazil, Brasília, DF, Brazil.
| | - Luc J W Evers
- Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands
| | - Tim Hahn
- Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands
| | - Lauren Bataille
- Michael J Fox Foundation for Parkinson's Research, New York, USA
| | - Jamie L Hamilton
- Michael J Fox Foundation for Parkinson's Research, New York, USA
| | - Max A Little
- Aston University, Birmingham, UK.,Media Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Yasuyuki Okuma
- Department of Neurology, Juntendo University Shizuoka Hospital, Izunokuni, Shizuoka, Japan
| | - Bastiaan R Bloem
- Radboud university medical center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.,Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands
| | - Marjan J Faber
- Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands.,Radboud university medical center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, The Netherlands
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Bhidayasiri R, Martinez-Martin P. Clinical Assessments in Parkinson's Disease: Scales and Monitoring. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2017; 132:129-182. [PMID: 28554406 DOI: 10.1016/bs.irn.2017.01.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Center of Excellence for Parkinson's Disease & Related Disorders, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Juntendo University, Tokyo, Japan.
| | - Pablo Martinez-Martin
- National Center of Epidemiology and CIBERNED, Carlos III Institute of Health, Madrid, Spain
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Use of Wearable Inertial Sensor in the Assessment of Timed-Up-and-Go Test: Influence of Device Placement on Temporal Variable Estimation. LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING 2017. [DOI: 10.1007/978-3-319-58877-3_40] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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