Upper extremity (UE) impairment is a common consequence of stroke, restricting daily activities. Clinical assessments such as the Fugl–Meyer Assessment (FMA) and the Action Research Arm Test (ARAT) are widely used but are typically therapist-administered. Inertial measurement units (IMUs) provide a portable, objective method to quantify upper limb kinematics and may therefore support scalable tele-rehabilitation. Yet, evidence on their reliability, validity, and clinical relevance remains limited. This study evaluated the test–retest reliability, discriminant validity (vs. healthy controls), and convergent validity (correlation with FMA and ARAT) of eleven IMU-derived kinematic metrics during a standardized drinking task in individuals with subacute stroke. Fifteen stroke patients and fifteen healthy controls performed the task wearing four IMUs on the upper limb and sternum. Both joint and end-point kinematics were derived using the Madgwick sensor fusion algorithm. Reliability was assessed through intraclass correlation coefficients (ICCs), discriminant validity through linear mixed models (LMMs), and convergent validity through Pearson’s correlations and regression models. Most metrics showed good to excellent reliability (ICC≥0.75), except for shoulder abduction (ICC=0.18) and maximum elbow angular velocity (ICC=0.65). All but shoulder abduction demonstrated significant discriminant validity. Movement time and measures of smoothness correlated moderately to strongly (r≥.67) with ARAT and FMA. These findings indicate that IMU-derived metrics during a standardized drinking task provide reliable, valid, and clinically meaningful insights into post-stroke motor status, and may offer supplementary information for movement assessment beyond conventional clinical scales.

Home-based rehabilitation is essential for stroke survivors, facilitating motor recovery and improving activities-of-daily-life performance. Recent advances in wearable technologies and machine learning promise to revolutionize home-based arm rehabilitation by providing detailed movement analysis. However, machine learning algorithms for arm movement identification are predominantly trained and tested in the same environments. Their ability to generalize to novel environments remains largely unknown, hindering practical applications. This paper investigates the ability of two established machine learning models to generalize a structured, lab-based environment to a more realistic, semi-structured kitchen environment. Twelve healthy participants performed various arm activities, involving three arm movement types (reaching, lifting, and pronation/supination). In addition to evaluating the generalization of movement identification, we compared algorithm performance for two different sensor configurations: four Inertial Measurement Units (IMUs) on the arm versus a single IMU on the wrist. We employed a Random Forest (RF) classifier and a hybrid deep learning model combining convolutional and recurrent neural networks, evaluating both subject-specific and group approaches. Trained in the structured environment, the RF classifier predicted activities in the semi-structured environment with 86.54% (subject-specific) and 77.37% (group) balanced accuracy, based on the four-sensor configuration, while the hybrid model reached 87.96% and 82.96% accuracy. The accuracy was lower with a single wrist IMU; the RF classifier showed a smaller decrease than the hybrid model. Our findings demonstrate that the investigated arm movement identification algorithms generalize well across environments even with the minimal sensor configuration, indicating the potential for future applications in home-based stroke rehabilitation.

Purpose: eHealth-based exercise therapies were developed to increase stroke patients’ adherence to home-based motor rehabilitation. However, these eHealth tools face a rapid decrease in use after a couple of weeks. This study investigates stroke patients’ motivation for home-based upper extremity rehabilitation with eHealth tools and their relation with Basic Psychological Needs. Materials and methods: This is a qualitative study using thematic analysis. We conducted semi-structured interviews with stroke patients with upper extremity motor impairments, who were discharged home from a rehabilitation centre, after they interacted with a novel eHealth coach demonstrator in their homes for five consecutive days. Results: We included ten stroke patients. Thematic analysis resulted in eight themes for home-based rehabilitation motivation: Curiosity, Rationale, Choice, Optimal challenge, Reference, Encouragement, Social Support and Trustworthiness. Those themes are embedded into three Basic Psychological Needs: “Autonomy”, “Competence”, and “Relatedness”. Conclusion: Eight motivational themes related to the three Basic Psychological Needs describe stroke patients’ motivation for home-based upper extremity rehabilitation. We recommend considering those themes when developing a home-based eHealth intervention for stroke patients to increase the alignment of eHealth tools to the patient’s needs and reduce motivational decreases in home-based rehabilitation.

Background: After mild stroke persistent balance limitations may occur, creating a risk factor for fear of falling, falls, and reduced activity levels. Objective. To investigate whether individuals in the chronic phase after mild stroke show balance and gait limitations, elevated fall risk, reduced balance confidence, and physical activity levels compared to healthy controls. Methods: An observational case-control study was performed. Main outcomes included the Mini-Balance Evaluation Systems Test (mini-BEST), Timed Up and Go (TUG), 10-m Walking Test (10-MWT), and 6-item version Activity-specific Balance Confidence (6-ABC) scale which were measured in 1 session. Objectively measured daily physical activity was measured for 7 consecutive days. Fall rate in daily life was recorded for 12 months. Individuals after a mild stroke were considered eligible when they: (1) sustained a transient ischemic attack or stroke longer than 6 months ago, resulting in motor and/or sensory loss in the contralesional leg at the time of stroke, (2) showed (near-) complete motor function, that is, ≥24 points on the Fugl-Meyer Assessment—Lower Extremity (range: 0-28). Results: Forty-seven healthy controls and 70 participants after mild stroke were included. Participants with stroke fell more than twice as often as healthy controls, had a 2 point lower median score on the mini-BEST, were 1.7 second slower on TUG, 0.6 km/h slower on the 10-MWT, and had a 12% lower 6-ABC score. Intensity for both total activity (8%) as well as walking activity (6%) was lower in the participants with stroke, while no differences were found in terms of duration. Conclusions: Individuals in the chronic phase after a mild stroke demonstrate persistent balance limitations and have an increased fall risk. Our results point at an unmet clinical need in this population.