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Anderson E, Lennon M, Kavanagh K, Weir N, Kernaghan D, Roper M, Dunlop E, Lapp L. Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review. Online J Public Health Inform 2024; 16:e57618. [PMID: 39110501 PMCID: PMC11339581 DOI: 10.2196/57618] [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: 02/21/2024] [Revised: 05/15/2024] [Accepted: 06/11/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. OBJECTIVE This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. METHODS The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. RESULTS In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. CONCLUSIONS All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.
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Affiliation(s)
- Euan Anderson
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Marilyn Lennon
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Kimberley Kavanagh
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom
| | - Natalie Weir
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - David Kernaghan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Marc Roper
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Emma Dunlop
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Linda Lapp
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
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Lyckegård Finn E, Carlsson H, Ericson P, Åström K, Brogårdh C, Wasselius J. The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period - an explorative observational study. J Neuroeng Rehabil 2024; 21:82. [PMID: 38769565 PMCID: PMC11103842 DOI: 10.1186/s12984-024-01381-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 05/10/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient's progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. METHODS Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. RESULTS 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman's rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 - 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. CONCLUSIONS The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability.
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Affiliation(s)
| | - Håkan Carlsson
- Department of Neurology, Rehabilitation Medicine, Memory Disorders and Geriatrics, Skåne University Hospital, Lund, Sweden
- Department of Health Sciences, Lund University, Lund, Sweden
| | | | - Kalle Åström
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Christina Brogårdh
- Department of Neurology, Rehabilitation Medicine, Memory Disorders and Geriatrics, Skåne University Hospital, Lund, Sweden
- Department of Health Sciences, Lund University, Lund, Sweden
| | - Johan Wasselius
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, 221 85, Sweden.
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
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Yang S, Garg NP, Gao R, Yuan M, Noronha B, Ang WT, Accoto D. Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:2998. [PMID: 36991709 PMCID: PMC10056111 DOI: 10.3390/s23062998] [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: 02/14/2023] [Revised: 03/04/2023] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study's detailed analysis can improve the usability of the assistive/rehabilitation robots.
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Affiliation(s)
- Sibo Yang
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Neha P. Garg
- Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore
| | - Ruobin Gao
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Meng Yuan
- Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore
| | - Bernardo Noronha
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Wei Tech Ang
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore
| | - Dino Accoto
- Department of Mechanical Engineering, Robotics, Automation and Mechatronics Division, KU Leuven, 3590 Diepenbeek, Belgium
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Hayden CD, Murphy BP, Hardiman O, Murray D. Measurement of upper limb function in ALS: a structure review of current methods and future directions. J Neurol 2022; 269:4089-4101. [PMID: 35612658 PMCID: PMC9293830 DOI: 10.1007/s00415-022-11179-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 11/29/2022]
Abstract
Measurement of upper limb function is critical for tracking clinical severity in amyotrophic lateral sclerosis (ALS). The Amyotrophic Lateral Sclerosis Rating Scale-revised (ALSFRS-r) is the primary outcome measure utilised in clinical trials and research in ALS. This scale is limited by floor and ceiling effects within subscales, such that clinically meaningful changes for subjects are often missed, impacting upon the evaluation of new drugs and treatments. Technology has the potential to provide sensitive, objective outcome measurement. This paper is a structured review of current methods and future trends in the measurement of upper limb function with a particular focus on ALS. Technologies that have the potential to radically change the upper limb measurement field and explore the limitations of current technological sensors and solutions in terms of costs and user suitability are discussed. The field is expanding but there remains an unmet need for simple, sensitive and clinically meaningful tests of upper limb function in ALS along with identifying consensus on the direction technology must take to meet this need.
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Affiliation(s)
- C D Hayden
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland. .,Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland. .,Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.
| | - B P Murphy
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland.,Advanced Materials and Bioengineering Research Centre (AMBER), Trinity College Dublin, Dublin 2, Ireland
| | - O Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.,Neurocent Directorate, Beaumont Hospital, Beaumont, Dublin 9, Ireland
| | - D Murray
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse St, Dublin 2, D02 R590, Ireland.,Neurocent Directorate, Beaumont Hospital, Beaumont, Dublin 9, Ireland
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Dutta D, Aruchamy S, Mandal S, Sen S. Poststroke Grasp Ability Assessment using an Intelligent Data Glove based on Action Research Arm Test: Development, Algorithms, and Experiments. IEEE Trans Biomed Eng 2021; 69:945-954. [PMID: 34495824 DOI: 10.1109/tbme.2021.3110432] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.
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Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, de Moura Neto E, de Souza JT, Wincker FC, Miranda LA, Hamamoto Filho PT, de Souza LAPS, Simões RP, de Oliveira Vidal EI, Bazan R. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept. Top Stroke Rehabil 2021; 29:331-346. [PMID: 34115576 DOI: 10.1080/10749357.2021.1926149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.
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Affiliation(s)
- Gustavo José Luvizutto
- Department of Applied Physical Therapy, Federal University of Triângulo Mineiro, Uberaba, Brazil
| | | | | | | | | | | | - Juli Thomaz de Souza
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | - Fernanda Cristina Wincker
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | - Luana Aparecida Miranda
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | | | | | - Rafael Plana Simões
- Department of Bioprocesses and Biotechnology, São Paulo State University, Botucatu, SP, Brazil
| | | | - Rodrigo Bazan
- Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
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Jiang S, Kang P, Song X, Lo B, Shull P. Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey. IEEE Rev Biomed Eng 2021; 15:85-102. [PMID: 33961564 DOI: 10.1109/rbme.2021.3078190] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
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