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.

Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach.

Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and the accompanying "shoulder load". To make an estimation of daily shoulder load, it is crucial to know which ADL are performed and how these are executed in the free-living environment (in terms of magnitude, frequency, and duration). The aim of this study was to develop and validate methodology for the classification of wheelchair related shoulder loading ADL (SL-ADL) from wearable sensor data. Ten able bodied participants equipped with five Shimmer sensors on a wheelchair and upper extremity performed eight relevant SL-ADL. Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. Overall, the trained algorithm performed well, with an accuracy of 98% and specificity of 99%. When reducing the input for training the network to data from only one sensor, the overall performance decreased to around 80% for all performance measures. The use of only forearm sensor data led to a better performance than the use of the upper arm sensor data. It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data.

A Scoping Review on Shoulder Injuries of Wheelchair Tennis Players: Potential Risk-Factors and Musculoskeletal Adaptations.

Wheelchair tennis players are prone to develop shoulder injuries, due to the combination of wheelchair propulsion, overhead activities and daily wheelchair activities. A methodical literature search was conducted to identify articles on shoulder complaints in wheelchair tennis, wheelchair sports and tennis. The aims were to identify (1) type of shoulder complaints; (2) possible risk factors for the development of shoulder injuries; (3) musculoskeletal adaptations in the shoulder joint in wheelchair tennis players. Fifteen papers were included in this review, five on wheelchair tennis, three on wheelchair sports and seven on tennis. Type of shoulder complaints were acromioclavicular pathology, osteoarthritic changes, joint effusion and rotator cuff tears. Possible risk factors for the development of shoulder injuries in wheelchair tennis are overhead movements, repetitive activation of the anterior muscle chain and internal rotators, as well as a higher spinal cord injury level. Muscular imbalance with higher values for the internal rotators, increase in external range of motion, decrease in internal range of motion and reduced total arc of motion were the most common proposed musculoskeletal adaptations due to an unbalanced load. These presented risk factors and musculoskeletal adaptations might help researchers, coaches and wheelchair tennis players to prevent shoulder injuries.