Assistive Technology最新文献

The Health App Review Tool (HART) is a novel assessment designed to match users with Alzheimer's disease or related dementias (ADRD) and caregivers to mobile applications that support health and wellness. The objectives of this study were to gather stakeholder feedback on the HART and then to implement revisions. Thirteen participants completed in-depth Think Aloud interviews. Participants shared qualitative feedback on each HART item. Participant feedback was analyzed via in-depth video-audio review. Feedback was implemented as actionable HART revisions. On average, the majority of participants rated items as "adequate"; however, qualitative findings indicated the need for improvement in conciseness, clarity, and understandability. Conciseness was addressed by combining related concepts into multi-items, clarity through the addition of specific examples, and understandability through improved verbiage. The HART has been refined from 106 items to 17 items through extensive revisions to the clarity, conciseness, and explanations provided throughout the assessment.

A current limitation in the development of robotic gait training interventions is understanding the factors that predict responses to treatment. The purpose of this study was to explore the application of an interpretable machine learning method, Bayesian Additive Regression Trees (BART), to identify factors influencing neuromuscular responses to a resistive ankle exoskeleton in individuals with cerebral palsy (CP). Eight individuals with CP (GMFCS levels I - III, ages 12-18 years) walked with a resistive ankle exoskeleton over seven visits while we measured soleus activation. A BART model was developed using a predictor set of kinematic, device, study, and participant metrics that were hypothesized to influence soleus activation. The model (R² = 0.94) found that kinematics had the largest influence on soleus activation, but the magnitude of exoskeleton resistance, amount of gait training practice with the device, and participant-level parameters also had substantial effects. To optimize neuromuscular engagement during exoskeleton training in individuals with CP, our analysis highlights the importance of monitoring the user's kinematic response, in particular, peak stance phase hip flexion and ankle dorsiflexion. We demonstrate the utility of machine learning techniques for enhancing our understanding of robotic gait training outcomes, seeking to improve the efficacy of future interventions.

This study aimed to investigate clinical stakeholders' acceptance of an immersive wheelchair simulator as a potential powered wheelchair skills training tool. Focus groups, conducted in four rehabilitation centers, were used to obtain a rich understanding of participants' experiences and beliefs. Then, a cross-sectional survey of the simulator acceptability for clinical practice was created. Twenty-three rehabilitation therapists and clinical program directors participated in the focus groups and thirty-three responded to the survey. Participants generally expressed that use of the simulator would be complementary to training in an actual powered wheelchair, and that it could be useful for challenging situations in rehabilitation centers (e.g. anxious clients; when there is uncertainty around their potential to drive a powered wheelchair; tasks that cannot be assessed in a real-life environment). They also provided suggestions to improve the simulator (e.g. more feedback during tasks; possibility of adjusting control settings such as speed and sensitivity; possibility of adding varied control interfaces). Feedback received from key stakeholders clearly indicated that the wheelchair simulator would be complementary to training provided in a real context of use. However, some important limitations must be addressed to improve the simulator and promote its adoption by clinical programs, therapists and clients.

Physical activity, particularly walking, is commonly used for the treatment of diseases such as low back pain. In this study, the effects of walking wearing the new ToneFit Reha training belt (TFR) were compared to both Nordic walking and regular walking. The TFR is intended to intensify the effects of walking through the integration of two adjustable resistance handles. Ten patients with low back pain performed regular walking, Nordic walking, and walking with the TFR in a movement laboratory. The kinematics of the trunk, upper extremities, and lower extremities were measured, and the activity of the trunk and upper extremity muscles recorded. Data were analyzed by repeated-measures ANOVA and paired t-test. Kinematics indicated that walking with the TFR introduces instability that was mitigated by a delayed peak trunk rotation (peak at 63.3% gait cycle, vs. 52.8% in walking (p = .001) and 51.0% in NW (p = .007)). Upper extremity kinematics (constrained elbow flexion, high peak shoulder abduction) showed movement patterns that need to be considered when training over a longer period. Increased muscle activity was observed especially for upper extremity muscles, when training with TFR. Overall, walking with the TFR was found to be a suitable therapy for use in a rehabilitation setting.

Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community.

This study aimed to assess the methodological and reporting quality of qualitative studies conducted in the field of lower limb orthoses (LLOs). The following electronic databases were searched from inception to 2022: PubMed, Scopus, ProQuest, WoS, Embase, the Cochrane Central Register of Controlled Trials, and RehabData. Two authors independently screened and selected the potential studies. The methodological quality of included studies was assessed using the Critical Appraisal Skills Programs qualitative checklist. In addition, the reporting quality of included studies was assessed using the Standards for Reporting Qualitative Research (SRQR) tool. The mean methodological quality score of included studies was 8 (from min = 2 to max = 9.5), and most of the studies had a score of more than 7.5. However, SRQR findings revealed that the overall reporting quality of included studies was not desirable in that the mean score was about 15.44 (from min = 6 to max = 19.5) out of 21. In total, the methodological quality of qualitative studies published in the field of LLOs was moderate. Further, the adherence of these studies to available reporting guidelines was unsatisfactory. As a result, when designing, performing, and reporting qualitative investigations, authors should pay more attention to these criteria.

Community-dwelling individuals with chronic stroke used a novel, portable rehabilitation system, mRehab, that uses a smartphone app coupled with 3D printed objects resembling daily use items. The objectives of this study include evaluating participant's approach and nature of engagement with mRehab and identifying factors that influenced the users' engagement with mRehab. An explanatory mixed-method approach was used. In the first phase, 16 participants used mRehab at home for six weeks; six participants were recruited from the first phase for in-depth interviews. Participants were categorized into High, Moderate, and Low Frequency groups based on their frequency of mRehab use. High frequency of use was not related to improved performance on clinical assessments; instead, High Frequency users more commonly initiated performance of new activities after the mRehab program compared to participants with lower frequency of use. Useful activities that are challenging and meaningful to the participants, and availability of objective feedback for self-monitoring were some of the motivators for mRehab use. Difficulty with time management, lack of caregiver availability, and difficulties with the design of the system posed as barriers to mRehab use. Tailoring home programs to the recipients' needs including perceived meaningfulness of the activities is key for long-term rehabilitation.