Improving patient outcomes in acute and subacute stroke using a wearable device-assisted rehabilitation system: a randomized controlled trial.

OBJECTIVE: Multidisciplinary rehabilitation facilitates post-stroke functional recovery, but is associated with resource and accessibility barriers. This study evaluated the combination of a wearable device-assisted system (WEAR) and conventional therapy for post-stroke rehabilitation. METHODS: This randomized, controlled, parallel group, clinical trial was conducted at two rehabilitation centers. A WEAR system was developed featuring sensors and application program-embedded smartphones. Stroke patients within 12 weeks of onset and modified Rankin Scale (mRS) scores of 2 to 4 were randomized into a wearable group (WG, WEAR + conventional rehabilitation) or control group (CG, conventional rehabilitation) for 90 days. The primary outcome was mRS score changes within 90 days. RESULTS: Among 127 stroke patients enrolled (76 men [59.8%]; mean age: 57.5 years), 63 and 64 patients were randomized to WG and CG, respectively. Both groups showed significant improvements in mRS scores. Between-group repeated measures analysis adjusted for sex, age and number of rehabilitation sessions showed greater improvement in mRS scores within 90 days in the WG than in the CG (estimate: 0.73). CONCLUSIONS: This combined WEAR and conventional rehabilitation approach may improve post-stroke functional recovery compared with conventional rehabilitation alone. The WEAR system permits remote monitoring and recording of rehabilitation in various settings.This clinical trial was retrospectively registered at www.clinicaltrials.gov with the Unique Identifier NCT04997408.

Alterations of sympathetic dynamics after atrial fibrillation ablation by analysis sympathetic nerve activity provide prognostic value for recurrence and mechanistic insights into ablation.

Background: Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation. Success is associated with autonomic function modulation; however, the relationship between the changes after ablation is not fully understood. We aimed to investigate the effect of ablation on autonomic modulation by skin sympathetic nerve activity (SKNA) using conventional electrocardiogram (ECG) electrodes and to predict the treatment success. Methods: We enrolled 79 patients. We recorded neuECG for 10 min at 10 kHz before and after ablation. The NeuECG was bandpass-filtered (500-1,000 Hz) and integrated at intervals of 100 ms (iSKNA). iSKNA was averaged over different time windows (1-, 5-,10-s; aSKNAs), and burst analyses were derived from aSKNAs to quantify the dynamics of sympathetic activities. AF recurrence after 3 months was defined as the study endpoint. Results: Sixteen patients experienced AF recurrence after the ablation. For burst analysis of 1-s aSKNA, the recurrence group had a higher bursting frequency than the non-recurrence group (0.074 ± 0.055 vs. 0.109 ± 0.067; p < 0.05) before ablation. The differences between pre- and post-ablation of firing duration longer than 2 s were more in the non-recurrence group (2.75 ± 6.41 vs. -1.41 ± 5.14; p < 0.05), while no significant changes were observed in the percentage of duration longer than 10 s using 5-s aSKNA. In addition, decreases in differences in firing frequency and percentage of both overall firing duration and longer firing duration (> 2 s) between pre- and post-ablation were independently associated with AF recurrence and more area under receiver operating characteristics (ROC) curve in combination with CHADS2 score (0.833). Conclusion: We demonstrated the applicability of neuECG for determining sympathetic modulation during AF ablation. Decreasing sympathetic activity is the key to successful ablation.

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network.

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Depth-Sensor-Based Monitoring of Therapeutic Exercises.

In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.