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Santicchi G, Stillavato S, Deriu M, Comi A, Cerveri P, Esposito F, Zago M. Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:3343. [PMID: 38894136 PMCID: PMC11174549 DOI: 10.3390/s24113343] [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: 04/20/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.
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Affiliation(s)
- Gabriele Santicchi
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Susanna Stillavato
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Marco Deriu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy; (M.D.); (P.C.)
| | - Aldo Comi
- Soccerment s.r.l, Viale Monza 259/265, 20126 Milan, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy; (M.D.); (P.C.)
| | - Fabio Esposito
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Matteo Zago
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
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2
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Wang WH, Hsu WS. Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments. SENSORS (BASEL, SWITZERLAND) 2023; 23:5913. [PMID: 37447763 DOI: 10.3390/s23135913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences.
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Affiliation(s)
- Wei-Hsun Wang
- Department of Orthopedic Surgery, Changhua Christian Hospital, Changhua 500209, Taiwan
- Department of Golden-Ager Industry Management, Chaoyang University of Technology, Taichung 413310, Taiwan
- College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Wen-Shin Hsu
- Department of Medical Information, Chung Shan Medical University, Taichung 402201, Taiwan
- Informatics Office Technology, Chung Shan Medical University Hospital, Taichung 402201, Taiwan
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3
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Khaliluzzaman M, Uddin A, Deb K, Hasan MJ. Person Recognition Based on Deep Gait: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:4875. [PMID: 37430786 DOI: 10.3390/s23104875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.
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Affiliation(s)
- Md Khaliluzzaman
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram 4318, Bangladesh
| | - Ashraf Uddin
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Kaushik Deb
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
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4
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Turner A, Hayes S, Sharkey D. The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development. SENSORS (BASEL, SWITZERLAND) 2023; 23:4800. [PMID: 37430717 DOI: 10.3390/s23104800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient's home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children's dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children's complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.
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Affiliation(s)
- Alexander Turner
- Department of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Stephen Hayes
- Department of Engineering, Nottingham Trent University, Nottingham NG4 2EA, UK
| | - Don Sharkey
- Department of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
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5
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Rahemtulla Z, Turner A, Oliveira C, Kaner J, Dias T, Hughes-Riley T. The Design and Engineering of a Fall and Near-Fall Detection Electronic Textile. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1920. [PMID: 36903036 PMCID: PMC10004402 DOI: 10.3390/ma16051920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Falls can be detrimental to the quality of life of older people, and therefore the ability to detect falls is beneficial, especially if the person is living alone and has injured themselves. In addition, detecting near falls (when a person is imbalanced or stumbles) has the potential to prevent a fall from occurring. This work focused on the design and engineering of a wearable electronic textile device to monitor falls and near-falls and used a machine learning algorithm to assist in the interpretation of the data. A key driver behind the study was to create a comfortable device that people would be willing to wear. A pair of over-socks incorporating a single motion sensing electronic yarn each were designed. The over-socks were used in a trial involving 13 participants. The participants performed three types of activities of daily living (ADLs), three types of falls onto a crash mat, and one type of near-fall. The trail data was visually analyzed for patterns, and a machine learning algorithm was used to classify the data. The developed over-socks combined with the use of a bidirectional long short-term memory (Bi-LSTM) network have been shown to be able to differentiate between three different ADLs and three different falls with an accuracy of 85.7%, ADLs and falls with an accuracy of 99.4%, and ADLs, falls, and stumbles (near-falls) with an accuracy of 94.2%. In addition, results showed that the motion sensing E-yarn only needs to be present in one over-sock.
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Affiliation(s)
- Zahra Rahemtulla
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Alexander Turner
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Carlos Oliveira
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Jake Kaner
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Tilak Dias
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Theodore Hughes-Riley
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
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6
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Ramazi R, Bowen MEL, Flynn AJ, Beheshti R. Developing Acute Event Risk Profiles for Older Adults with Dementia in Long-Term Care Using Motor Behavior Clusters Derived from Deep Learning. J Am Med Dir Assoc 2022; 23:1977-1983.e1. [PMID: 35594943 DOI: 10.1016/j.jamda.2022.04.009] [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: 09/17/2021] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVES This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents. DESIGN Longitudinal observational study. SETTING AND PARTICIPANTS A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities. METHODS More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event. RESULTS Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88-0.91; specificity range = 0.71-0.88). CONCLUSION AND IMPLICATIONS Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population.
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Affiliation(s)
- Ramin Ramazi
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
| | - Mary Elizabeth Libbey Bowen
- School of Nursing, University of Delaware, Newark, DE, USA; Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA; Coatesville Veterans Affairs Medical Center, Coatesville, PA, USA
| | - Aidan J Flynn
- Coatesville Veterans Affairs Medical Center, Coatesville, PA, USA
| | - Rahmatollah Beheshti
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA
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7
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Zhu M, Men Q, Ho ESL, Leung H, Shum HPH. A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. J Med Syst 2022; 46:76. [PMID: 36201114 PMCID: PMC9537228 DOI: 10.1007/s10916-022-01857-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022]
Abstract
Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.
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Affiliation(s)
- Manli Zhu
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Qianhui Men
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Edmond S L Ho
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - Howard Leung
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Hubert P H Shum
- Department of Computer Science, Durham University, Durham, UK.
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8
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Parashar A, Shekhawat RS, Ding W, Rida I. Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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9
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Sethi D, Bharti S, Prakash C. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work. Artif Intell Med 2022; 129:102314. [DOI: 10.1016/j.artmed.2022.102314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/15/2022]
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Abstract
Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed.
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11
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E. B, D. B, Elumalai VK, R. V. Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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12
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Vilas-Boas MDC, Rocha AP, Cardoso MN, Fernandes JM, Coelho T, Cunha JPS. Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1350-1362. [PMID: 34252029 DOI: 10.1109/tnsre.2021.3096433] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.
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13
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Zanetti R, Arza A, Aminifar A, Atienza D. Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices. IEEE Trans Biomed Eng 2021; 69:265-277. [PMID: 34166183 DOI: 10.1109/tbme.2021.3092206] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operators cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. METHODS Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. RESULTS We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. CONCLUSION We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. SIGNIFICANCE The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.
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14
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Lu Y, Wang H, Qi Y, Xi H. Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102279] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Butt AH, Cavallo F, Maremmani C, Rovini E. Biomechanical parameters assessment for the classification of Parkinson Disease using Bidirectional Long Short-Term Memory .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5761-5764. [PMID: 33019283 DOI: 10.1109/embc44109.2020.9176051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Nowadays objective and efficient assessment of Parkinson Disease (PD) with machine learning techniques is a major focus for clinical management. This work presents a novel approach for classification of patients with PD (PwPD) and healthy controls (HC) using Bidirectional Long Short-Term Neural Network (BLSTM). In this paper, the SensHand and the SensFoot inertial wearable sensors for upper and lower limbs motion analysis were used to acquire motion data in thirteen tasks derived from the MDS-UPDRS III. Sixty-four PwPD and fifty HC were involved in this study. One hundred ninety extracted spatiotemporal and frequency parameters were applied as a single input against each subject to develop a recurrent BLSTM to discriminate the two groups. The maximum achieved accuracy was 82.4%, with the sensitivity of 92.3% and specificity of 76.2%. The obtained results suggest that the use of the extracted parameters for the development of the BLSTM contributed significantly to the classification of PwPD and HC.
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16
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Kiprijanovska I, Gjoreski H, Gams M. Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning. SENSORS 2020; 20:s20185373. [PMID: 32961750 PMCID: PMC7571106 DOI: 10.3390/s20185373] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/14/2020] [Accepted: 09/18/2020] [Indexed: 12/25/2022]
Abstract
Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.
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Affiliation(s)
- Ivana Kiprijanovska
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
- Correspondence:
| | - Hristijan Gjoreski
- Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia;
| | - Matjaž Gams
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
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17
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Dot T, Quijoux F, Oudre L, Vienne-Jumeau A, Moreau A, Vidal PP, Ricard D. Non-Linear Template-Based Approach for the Study of Locomotion. SENSORS 2020; 20:s20071939. [PMID: 32235667 PMCID: PMC7180476 DOI: 10.3390/s20071939] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/17/2020] [Accepted: 03/26/2020] [Indexed: 12/25/2022]
Abstract
The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.
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Affiliation(s)
- Tristan Dot
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Flavien Quijoux
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- ORPEA Group, F-92813 Puteaux, France
| | - Laurent Oudre
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- Correspondence: ; Tel.: +33-1-49-40-40-63
| | - Aliénor Vienne-Jumeau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Albane Moreau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
| | - Pierre-Paul Vidal
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Hangzhou Dianzi University, Hangzhou C-310005, China
| | - Damien Ricard
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Ecole de Santé des Armées, F-75005 Paris, France
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