# SCH: INT: An Adaptive Robotic Hand Orthosis with Multimodal Sensing and Continuous Learning

> **NIH NIH R01** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2022 · $277,193

## Abstract

We propose to develop a wearable robotic hand orthosis, able to act as a multimodal sensory platform
 and to physically assist functional hand movement. Working with stroke patients, we will deploy this
 orthosis for extended use in retraining the upper limb to improve the ability to perform activities of daily
living in a simulated domestic environment. In these sessions, we will test our platform in two possible
roles: an active orthosis as well as a rehabilitation device.
To enable extended use of the orthosis for functional tasks, we rely on two novel approaches. The first is
that of a robotic hand orthosis seen as a platform for both actuation and multimodal sensing. This will be
the first device to collect multimodal data on the assisted impaired hand during functional tasks,
combining information such as applied motor force, finger joint angles, fingertip pose, applied contact
pressure, muscle activity, etc. Based on this data, we propose a multimodal learning mechanism for
inferring user intent in order to operate the device. As a significant innovation, this approach lends itself to
continuous learning: different sensing modalities can inform and train each other during use, in the
absence of manually provided ground truth. Thus, we aim to remove a longstanding challenge for
user-controlled assistive devices: the need for repeated calibration and supervision.
The second novel approach is that of an assistive platform used to characterize and develop mitigation
strategies for unwanted and abnormal muscle activations, an important and currently unaddressed
challenge for achieving functional movement of the combined proximal and distal upper limb. These
phenomena, which directly oppose training movements, have generally been ignored in the design of
robotic devices, or addressed through blunt methods such as a general increase of the applied force in
order to overcome spasticity, or exclusion of the proximal upper limb from the task in order to avoid
synergies. Our sensing capabilities will allow us to study how applied assistive forces affects these
phenomena, and develop targeted strategies to mitigate them.

## Key facts

- **NIH application ID:** 10455499
- **Project number:** 5R01NS115652-04
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Matei Ciocarlie
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $277,193
- **Award type:** 5
- **Project period:** 2019-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455499

## Citation

> US National Institutes of Health, RePORTER application 10455499, SCH: INT: An Adaptive Robotic Hand Orthosis with Multimodal Sensing and Continuous Learning (5R01NS115652-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10455499. Licensed CC0.

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