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

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

IMPORTANCE

Stroke is the leading cause of long-term disability in the United States. Providers have no robust tools to objectively and accurately measure the activity of people with stroke living at home.

OBJECTIVE

To explore the integration of validated upper extremity assessments poststroke within an activity recognition system.

DESIGN

Exploratory descriptive study using data previously collected over 3 mo to report on algorithm testing and assessment integration.

SETTING

Data were collected in the homes of community-dwelling participants.

PARTICIPANTS

Participants were at least 6 mo poststroke, were able to ambulate with or without an assistive device, and self-reported some difficulty using their arm in everyday activities.

OUTCOMES AND MEASURES

The activity detection algorithm's accuracy was determined by comparing its activity labels with manual labels. The algorithm integrated assessment by describing the quality of upper extremity movement, which was determined by reporting extent of reach, mean and maximum speed during movement, and smoothness of movement.

RESULTS

Sixteen participants (9 women, 7 men) took part in this study, with an average age of 63.38 yr (SD = 12.84). The algorithm was highly accurate in correctly identifying activities, with 87% to 95% accuracy depending on the movement. The algorithm was also able to detect the quality of movement for upper extremity movements.

CONCLUSIONS AND RELEVANCE

The algorithm was able to accurately identify in-kitchen activities performed by adults poststroke. Information about the quality of these movements was also successfully calculated. This algorithm has the potential to supplement clinical assessments in treatment planning and outcomes reporting. Plain-Language Summary: This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. The algorithm accurately identified activities that were performed in the kitchen by adults poststroke. The algorithm also identified the quality of upper extremity movements of people poststroke who were living at home.

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

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.

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants' homes over three months. Given the extensive amount of data, only a portion of the participants' data was used for this specific analysis. 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, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional-de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.

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

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.