Novel clinically-relevant assessment of upper extremity movement using depth sensors

Proffitt, Rachel; Ma, Mengxuan; Skubic, Marjorie

doi:10.1080/10749357.2021.2006981

Cited by 5 publications

(14 citation statements)

References 21 publications

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“…More than 1/3 of the articles included have used or created VR or virtual environment as part of their intervention. 12,15,18,20,28,29,33,35,36,38,40,42,[45][46][47]51,56,58,69,72,78,79,82,84,89,91 VR enables the provision of rehabilitation offering rich experience and credibility in alternative environments such as at home. This minimizes auditory and visual distractions that possibly can happen in communal health care settings.…”

Section: Discussionmentioning

confidence: 99%

“…92 This emerging technology is widely being integrated in various medical fields. 93 Twenty-six articles 12,15,18,20,28,29,33,35,36,38,40,42,[45][46][47]51,56,58,69,72,78,79,82,84,89,91 used or created a VR intervention or a virtual environment for home-based upper limb rehabilitation after stroke.…”

Section: Themesmentioning

confidence: 99%

“…30,50 Sensors Sensors were also used to detect the motion or movement of the upper extremities during the practice of rehabilitative interventions. Twenty-two articles 15,16,18,21,22,26,31,34,39,45,46,50,51,54,57,58,61,78,82,84,87,89 discussed the use of upper extremity-sensing digital technologies in home rehabilitation of stroke patients.…”

Section: Mobile Technologymentioning

confidence: 99%

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The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

2023

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Objective To identify, map and synthesize the extent and nature of existing studies on the use of home-based digital technology to support post-stroke upper limb rehabilitation. Data sources A comprehensive literature search was completed between 30 May 2022 and 05 April 2023, from seven online databases (CINAHL, Cochrane Library, PubMed, ScienceDirect, IEEExplore, Web of Science and PEDro), Google Scholar and the reference lists of already identified articles. Methods A scoping review was conducted according to Arksey and O’Malley (2005), and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. All English-language studies reporting on the use of home-based digital technology to support upper limb post-stroke rehabilitation were eligible for inclusion. Results The search generated a total of 1895 records, of which 76 articles met the inclusion criteria. Of these, 52 were experimental studies and the rest, qualitative, case series and case studies. Of the overall 2149 participants, 2028 were stroke survivors with upper limb impairment. The majority of studies were aimed at developing, designing and/or assessing the feasibility, acceptability and efficacy of a digital system for poststroke upper limb rehabilitation in home settings. The thematic analysis found six major categories: Tele-rehabilitation ( n = 29), games ( n = 45), virtual reality (n = 26), sensor (n = 22), mobile technology (n = 22), and robotics (n = 8). Conclusion The digital technologies used in post-stroke upper limb rehabilitation were multimodal, and system-based comprising telerehabilitation, gamification, virtual reality, mobile technology, sensors and robotics. Furthermore, future research should focus to determine the effectiveness of these modalities.

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Section: Discussionmentioning

confidence: 99%

Section: Themesmentioning

confidence: 99%

Section: Mobile Technologymentioning

confidence: 99%

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The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

2023

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“…First, the kinematics study can provide accurate objective information of the upper extremity motion. Second, the analysis of kinematics has also been considered more sensitive than using clinical scales [26].…”

Section: Motivationmentioning

confidence: 99%

“…First, they can provide accurate objective information of the upper extremity motion. Second, kinematic measures are more sensitive than traditional clinical measures [26].…”

Section: Rehabilitation Technology and Objective Kinematic Assessmentmentioning

confidence: 99%

Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

Ma¹

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Stroke is the leading cause of serious, long-term disabilities among which deficits in motor abilities in arms or legs are most common. Those who suffer a stroke can recover through effective rehabilitation which is delicately personalized. To achieve the best personalization, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently. Traditionally, rehabilitation involves patients performing exercises in clinics where clinicians oversee the procedure and evaluate patients' recovery progress. Following the in-clinic visits, additional home practices are tailored and assigned to patients. The in-clinic visits are important to evaluate recovery progress. The information collected can then help clinicians customize home practices for stroke patients. However, as the number of in-clinic sessions is limited by insurance policies, the recovery information collected in-clinic is often insufficient. Meanwhile, the home practice programs report low adherence rates based on historic data. Given that clinicians rely on patients to self-report adherence, the actual adherence rate could be even lower. Despite the limited feedback clinicians could receive, the measurement method is subjective as well. In practice, classic clinical scales are mostly used for assessing the qualities of movements and the recovery status of patients. However, these clinical scales are evaluated subjectively with only moderate inter-rater and intra-rater reliabilities. Taken together, clinicians lack a method to get sufficient and accurate feedback from patients, which limits the extent to which clinicians can personalize treatment plans. This work aims to solve this problem. To help clinicians obtain abundant health information regarding patients' recovery in an objective approach, I've developed a novel kinematic assessment toolchain that consists of two parts. The first part is a tool to evaluate stroke patients' motions collected in a rehabilitation game setting. This kinematic assessment tool utilizes body-tracking in a rehabilitation game. Specifically, a set of upper body assessment measures were proposed and calculated for assessing the movements using skeletal joint data. Statistical analysis was applied to evaluate the quality of upper body motions using the assessment outcomes. Second, to classify and quantify home activities for stroke patients objectively and accurately, I've developed DARAS, a daily activity recognition and assessment system that evaluates daily motions in a home setting. DARAS consists of three main components: daily action logger, action recognition part, and assessment part. The logger is implemented with a Foresite system to record daily activities using depth and skeletal joint data. Daily activity data in a realistic environment were collected from sixteen post-stroke participants. The collection period for each participant lasts three months. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network fuses the prediction outputs from customized 3D Convolutional-De-Convolutional, customized Region Convolutional 3D network and a proposed Region Hierarchical Co-occurrence network which learns rich spatial-temporal features from either depth data or joint data. The per-frame precision and the per-action precision were 0.819 and 0.838, respectively, on the validation set. For the recognized actions, the kinematic assessments were performed using the skeletal joint data, as well as the longitudinal assessments. The results showed that, compared with non-stroke participants, stroke participants had slower hand movements, were less active, and tended to perform fewer hand manipulation actions. The assessment outcomes from the proposed toolchain help clinicians to provide more personalized rehabilitation plans that benefit patients.

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The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

Wei,

2023

Sensors

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The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

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Novel clinically-relevant assessment of upper extremity movement using depth sensors

Cited by 5 publications

References 21 publications

The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

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