Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications.

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.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial.

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Measuring Movement Quality of the Stroke-Impaired Upper Extremity with a Wearable Sensor: Toward a Smoothness Metric for Home Rehabilitation Exercise Programs.

Remote patient monitoring systems show promise for assisting stroke patients in home exercise programs. While these systems typically measure exercise repetitions in order to monitor compliance, a key goal of therapists is to also monitor movement quality. Here we develop a measure of movement quality - Peak Intensity - that is a measure of movement smoothness that is implementable with a wrist-worn inertial measurement unit (IMU) in the context of performing repetitions of an upper extremity exercise. To calculate Peak Intensity, we assume we have an accurate count of the number of exercise repetitions in an exercise set, then calculate Peak Intensity as the total number of movement peaks from the continuous stream of IMU data generated across the set, divided by the number of repetitions. Using wrist-worn IMU measurements from 19 participants with chronic stroke performing a sample exercise in which they picked up and moved blocks across a divider (i.e. the Box and Blocks Test) we show that Peak Intensity is moderately correlated with a widely used measure of movement quality, the Quality of Movement score of the Motor Activity Log. Peak Intensity is also strongly correlated with a measure of hand function (the BBT score itself), but is more sensitive at greater levels of impairment. Finally, we show Peak Intensity can be validly derived from either wrist acceleration or angular velocity. These results suggest Peak Intensity could serve as an indicator of movement exercise quality for therapists monitoring home rehabilitation, and, potentially, as a means to provide augmented feedback to patients about their exercise quality.