Unsupervised Home-based Rehabilitation Research Articles

Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation.

Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end, the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras.

Effectiveness of a home-based telerehabilitation system in patients after total hip arthroplasty: study protocol of a randomized controlled trial

BackgroundThe demand for total hip arthroplasty (THA) is quickly rising given the escalating global incidence of hip osteoarthritis, and it is widely accepted that the post-surgery rehabilitation is key to optimize outcomes. The overall objective of this study is to evaluate the effectiveness of a new telerehabilitation solution, ReHub, for the physical function and clinical outcome improvement following THA. The specific aims of this manuscript are to describe the study design, protocol, content of interventions, and primary and secondary outcomes and to discuss the clinical rehabilitation impact of the expected experimental results.Methods/designThis prospective, randomized, controlled, parallel-group trial will include 56 patients who had undergone primary THA. Patients are randomized to a control group (standard rehabilitation during the 2-week stay in the rehabilitation clinic followed by 3 weeks of unsupervised home-based rehabilitation) or an experimental group (standard rehabilitation during the 2-week stay in the rehabilitation clinic followed by 3 weeks of home-based ReHub-assisted telerehabilitation). The primary outcome is physical performance assessed through the Timed Up-and-Go (TUG) test. Secondary outcomes include independence level, pain intensity, hip disability, hip range of motion, muscle strength, and patient’s perception of clinical improvement.DiscussionProving the clinical and cost-effectiveness of a home-based telerehabilitation program for physical and muscle function following THA could support its systematic incorporation in post-surgical rehabilitation protocols, which should be tailored to the individual and collective needs.Trial registrationClinicalTrial.gov NCT04176315. Registered on 22 November 2019

Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients.

Virtual home-based rehabilitation is an emerging area in stroke rehabilitation. Functional assessment tools are essential to monitor recovery and provide current function-based rehabilitation. We developed the Fugl-Meyer Assessment (FMA) tool using Kinect (Microsoft, USA) and validated it for hemiplegic stroke patients. Forty-one patients with hemiplegic stroke were enrolled. Thirteen of 33 items were selected for upper extremity motor FMA. One occupational therapist assessed the motor FMA while recording upper extremity motion with Kinect. FMA score was calculated using principal component analysis and artificial neural network learning from the saved motion data. The degree of jerky motion was also transformed to jerky scores. Prediction accuracy for each of the 13 items and correlations between real FMA scores and scores using Kinect were analyzed. Prediction accuracies ranged from 65% to 87% in each item and exceeded 70% for 9 items. Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect (Pearson’s correlation coefficient = 0.873, P<0.0001) and those between total upper extremity scores (66 in full score) and scores using Kinect (26 in full score) (Pearson’s correlation coefficient = 0.799, P<0.0001). Log transformed jerky scores were significantly higher in the hemiplegic side (1.81 ± 0.76) compared to non-hemiplegic side (1.21 ± 0.43) and showed significant negative correlations with Brunnstrom stage (3 to 6; Spearman correlation coefficient = -0.387, P = 0.046). FMA using Kinect is a valid way to assess upper extremity function and can provide additional results for movement quality in stroke patients. This may be useful in the setting of unsupervised home-based rehabilitation.

A systematic review of measures of self-reported adherence to unsupervised home-based rehabilitation exercise programmes, and their psychometric properties

BackgroundAdherence is an important factor contributing to the effectiveness of exercise-based rehabilitation. However, there appears to be a lack of reliable, validated measures to assess self-reported adherence to prescribed but...