Analysis and Quantification of Repetitive Motion in Long-Term Rehabilitation

Computer vision for kinematic metrics of the drinking task in a pilot study of neurotypical participants

Assessment of the upper limb is critical to guiding the rehabilitation cycle. Drawbacks of observation-based assessment include subjectivity and coarse resolution of ordinal scales. Kinematic assessment gives rise to objective quantitative metrics, but uptake is encumbered by costly and impractical setups. Our objective was to investigate feasibility and accuracy of computer vision (CV) for acquiring kinematic metrics of the drinking task, which are recommended in stroke rehabilitation research. We implemented CV for upper limb kinematic assessment using modest cameras and an open-source machine learning solution. To explore feasibility, 10 neurotypical participants were recruited for repeated kinematic measures during the drinking task. To investigate accuracy, a simultaneous marker-based motion capture system was used, and error was quantified for the following kinematic metrics: Number of Movement Units (NMU), Trunk Displacement (TD), and Movement Time (MT). Across all participant trials, kinematic metrics of the drinking task were successfully acquired using CV. Compared to marker-based motion capture, no significant difference was observed for group mean values of kinematic metrics. Mean error for NMU, TD, and MT were - 0.12 units, 3.4 mm, and 0.15 s, respectively. Bland-Altman analysis revealed no bias. Kinematic metrics of the drinking task can be measured using CV, and preliminary findings support accuracy. Further study in neurodivergent populations is needed to determine validity of CV for kinematic assessment of the post-stroke upper limb.

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation

This paper proposes a free dataset, available at the following link,¹named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, depth videos, and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject's performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise.¹https://univpm-my.sharepoint.com/:f:/g/personal/p008099_staff_univpm_it/EiwbKIzk6N9NoJQx4J8aubIBx0o7tIa1XwclWp1NmRkA-w?e=F3jtBk.