The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call “Hand Activity Recognition through using a Convolutional neural network with Spectrograms” (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R² = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.

Many medical conditions impair proprioception but there are few easy-to-deploy technologies for assessing proprioceptive deficits. Here, we developed a method—called “OpenPoint”—to quantify upper extremity (UE) proprioception using only a webcam as the sensor. OpenPoint automates a classic neurological test: the ability of a person to use one hand to point to a finger on their other hand with vision obscured. Proprioception ability is quantified with pointing error in the frontal plane measured by a deep-learning-based, computer vision library (MediaPipe). In a first experiment with 40 unimpaired adults, pointing error significantly increased when we replaced the target hand with a fake hand, verifying that this task depends on the availability of proprioceptive information from the target hand, and that we can reliably detect this dependence with computer vision. In a second experiment, we quantified UE proprioceptive ability in 16 post-stroke participants. Individuals post stroke exhibited increased pointing error (p < 0.001) that was correlated with finger proprioceptive error measured with an independent, robotic assessment (r = 0.62, p = 0.02). These results validate a novel method to assess UE proprioception ability using affordable computer technology, which provides a potential means to democratize quantitative proprioception testing in clinical and telemedicine environments.

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic activities of daily living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide an assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, the sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at the 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3 months were noted (r = 0.619, r = 0.661, p < 0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r = 0.690, r = 0.815, p < 0.05). Multiple linear regression analyses revealed a change in average bilateral total RSA of 0.1 unit from admission to the 3-month follow-up correlated with a respective change in the FIM self-care score of 2.011 points (95%CI: 0.663–3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

The elderly population experiences a decline in upper extremity range of motion (ROM), impairing activities of daily living. The primary mode of quantification is by goniometer measurement. In this cross-sectional observation study, we investigate a sensor-acquired reachable workspace for assessing shoulder ROM decline in an elderly population in comparison to traditional measurements. Sixty-one healthy subjects aged ≥ 65 years were included and compared to a cohort of 39 younger subjects, aged 20 to 64. A sensor acquired reachable workspace using a Kinect motion capture camera measured the maximum reaching ability of both arms while in a seated position, measured in m² and normalized to arm length to calculate a novel score defined as a relative surface area. This score approximates range of motion in the upper extremity. This measurement was compared to goniometer measurements, including active ROM in shoulder flexion and abduction. Total RSA shows moderate to strong correlation between goniometer in flexion and abduction in the dominant arm (R = 0.790 and R = 0.650, P < .001, respectively) and moderate correlations for the nondominant arm (R = 0.622 and R = 0.615, P < .001). Compared to the younger cohort, the elderly population demonstrated significantly reduced total RSA in the dominant arm (mean_elderly = 0.774, SD = 0.09; mean_younger = 0.830, SD = 0.07, P < .001), with significant reductions in the upper lateral quadrant in both arms (dominant: mean_elderly = 0.225, SD = 0.04; mean_younger = 0.241, SD = 0.01; P < .001; nondominant: mean_elderly = 0.213, SD = 0.03; mean_younger = 0.228, SD = 0.01; P = .004). The test-retest reliability was strong for both dominant and nondominant total RSA (ICC > 0.762). The reachable workspace demonstrates promise as a simple and quick tool for clinicians to assess detailed and quantitative active shoulder ROM decline in the elderly population.

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic Activities of Daily Living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3-month were noted (r=0.619, r=0.661, p<0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r=0.690, r=0.815, p<0.05). Multiple linear regression analyses revealed a change in bilateral total RSA of 0.1 unit from admission to 3-month follow up correlated with respective change in FIM self-care score of 2.011 points (95%CI: 0.663-3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

Technologies for home movement rehabilitation after stroke are rapidly expanding. However, for consumers, the number and nature of available products are unclear, and the information provided by device manufacturers varies widely. To understand this landscape, we conducted a mixed-methods, descriptive study in which we used the U.S. Food and Drug Administration (FDA) database to identify interactive devices for stroke rehabilitation suitable for home use. We then surveyed 13 individuals with stroke to determine what information they most wanted about home-based rehabilitation devices and contacted manufacturers to obtain those details. Thirteen FDA codes were associated with stroke rehabilitation devices, encompassing 57 devices produced by 40 companies. Nearly half were categorized under two codes: QKC (interactive rehabilitation exercise devices) and GZI (neuromuscular stimulators). Among devices for which information was available, 71% were listed after 2015, and 23% cost under $1000. The top information priorities for individuals with stroke were required usage to achieve therapeutic benefit, expected benefit, ease of use, and motivational features. Despite repeated outreach, only 45% of companies responded to our queries; among those that did, details were vague and variable. These results confirm that a large and growing number of FDA-listed devices are now available for home-based post-stroke motor rehabilitation. We further identify a need to establish industry standards for reporting ease of use, motivational effectiveness, and dose–response characteristics to help the intended consumers select appropriate technologies. The curated dataset generated in this study is provided as a resource for future work and may support the development of accurate Artificial Intelligence-based interfaces for identifying and comparing rehabilitation devices.