Gamification of Physical Therapy Exercises Using Commercial Entertainment Content: A Safety and Feasibility Study.

Independent physiotherapy at home is a crucial element of rehabilitative care for a wide range of conditions as it constitutes a large portion of the overall therapy dose. However, up to 80% of individuals who are prescribed at-home physiotherapy do not consistently adhere to their treatment schedule, resulting in poor treatment outcomes. This is likely due to a lack of motivation and progress tracking in the current standard of care. We have developed a novel software prototype that allows users to control commercial entertainment content, such as video games or interactive music videos, with their movements during physiotherapy. By connecting therapy to proven entertainment content, we aim to improve on the current motivational deficits. This study investigated the safety and feasibility of this concept in a controlled environment over four physical therapy sessions with seven patients suffering from musculoskeletal and neurological conditions. As a secondary outcome, patients were asked about their enjoyment, perceived competence and effort using the Intrinsic Motivation Inventory (IMI) questionnaire. All participants were able to interact with the presented entertainment content and completed the study with no adverse events. Despite the diversity in pathology, age and training scenarios, the entertainment content maintained the patients' enjoyment with a high average rate of 6/7 on the IMI scale. Interacting with commercial entertainment content by doing physical therapy exercises was feasible, safe, and well-received over the six-week study period.

Movement Reminders to Encourage Arm Use During Daily Life in Stroke Patients.

Stroke is a leading cause of long-term disability, such as loss of upper limb function. Active arm movement and frequent practice are essential to regain such function. Wearable sensors that trigger individualized movement reminders can promote awareness of the affected limb during periods of inactivity. This study investigated the immediate effect of vibrotactile reminders based on activity counts on affected arm use, the evolution of the effect throughout a 6-week intervention at home, and whether the time of the day influences the response to the reminder. Thirteen participants who experienced a unilateral ischemic stroke were included in the analysis. Activity counts were found to increase significantly after receiving a reminder. The immediate effect of receiving a reminder was maintained throughout the day as well as during the study duration of 6 weeks. In conclusion, wearable activity trackers with a feature to trigger individualized vibrotactile reminders could be a promising rehabilitation tool to increase arm activity of the affected side in stroke patients in their home environment.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke.

Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R 2 = 0.93; range reported in previous studies: 0.61-0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation.

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.