# SCH: A Sensing Platform Monitoring Interactions with Daily Objects to Assess Real-World Motor Performance in Stroke Survivors

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2024 · $290,344

## Abstract

Stroke is the leading cause of disability in adults worldwide. Upper-limb paresis is the most common 
impairment post-stroke. The ultimate goal of stroke rehabilitation is to improve patients’ motor 
performance in their home and community settings (i.e., what patients actually do). However, current 
clinical standards to monitor patients’ recovery process are limited to assessing patients’ motor capacity 
observed in the clinic (i.e., what patients are capable of doing). Wrist-worn accelerometers have been 
considered as a potential solution but criticized for providing a limited view of upper-limb performance. 
Therefor, the research and clinical communities have emphasized the need for a technological solution to 
support a more comprehensive understanding of stroke survivors’ motor performance. 
In this work, we propose to develop a novel multi-modal sensing platform to monitor important elements of 
upper-limb motor performance: the amount, type, and quality of movements. To that end, we introduce a 
new kind of sensing technology, namely Body Channel Identification (BCID), that can accurately and 
reliably track human interactions with the environment and, thus, human behaviors. In our setting, 
everyday objects are instrumented with small, inexpensive, batteryless BCID tags that can be powered by 
and communicate with wrist-worn devices (so-called readers) by exploiting the human body as the signal 
transfer channel during tactile interactions. The system provides multi-modal data, including the object ID, 
binary time-series of interaction patterns (contact vs. no contact), kinetic data from an optional pressure 
sensor embedded in the tag, and kinematic data from the inertial measurement unit on the wrist-worn 
readers. Leveraging data obtained from 50 stroke survivors and ten healthy subjects, we propose to 
develop a unique set of machine learning algorithms to process these data to taxonomically identify 
important types of upper-limb movements relevant to stroke rehabilitation, which are further processed to 
assess the quality and amount of movements performed. Finally, we investigate the relationship between 
the motor capacity observed in the clinic vs. motor performance outside the clinic, a topic that has been 
deemed critical in stroke rehabilitation but infeasible due to technical limitations. We believe the proposed 
research will lay the technological groundwork to open up new research and clinical opportunities, leading 
to key scientific discoveries to transform current practices of stroke rehabilitation.

## Key facts

- **NIH application ID:** 10920448
- **Project number:** 5R01HD114147-02
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Sunghoon Lee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $290,344
- **Award type:** 5
- **Project period:** 2023-09-05 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10920448

## Citation

> US National Institutes of Health, RePORTER application 10920448, SCH: A Sensing Platform Monitoring Interactions with Daily Objects to Assess Real-World Motor Performance in Stroke Survivors (5R01HD114147-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10920448. Licensed CC0.

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