# SocialBit: Establishing the accuracy of a wearable sensor to detect social interactions after stroke

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $597,152

## Abstract

Stroke survivors are vulnerable to reduced social interactions. Reduced interactions are related to worse
physical recovery after stroke. Enhancing social interactions after stroke may be one of the most
powerful strategies to improve stroke recovery. Social interactions are defined as the synchronous
interactions, commonly verbal, between individuals who are usually co-present in the same physical
location. Current ways to detect social interactions rely on self-report, which cannot be performed reliably
by patients with language or cognitive deficits. Patients with such deficits are most vulnerable to social
isolation. This project introduces a new wearable social sensor, SocialBit, that can detect audio
signatures of social interactions in real-world settings. Our preliminary data show that SocialBit can
detect social interactions accurately (~95%), and it can do so by processing select audio features without
storing raw audio data. Therefore, the technology detects and measures the duration of the social
interaction while preserving the privacy of the content during the interaction. Based on these findings, we
have developed a research plan to establish the usefulness of SocialBit in stroke survivors in the
immediate post-stroke period. The post-stroke period is apt for such a study because 1) patients are
vulnerable to social deprivation in this time period, and 2) the bounded nature of an inpatient setting
provides an ideal environment to test SocialBit against a ground truth of directly observed social
interactions. Our central hypothesis is that SocialBit can accurately detect social interactions in stroke
survivors in inpatient settings. This project is primarily designed to establish the accuracy of SocialBit to
detect social interaction in patients with varying deficits against the ground truth of video-assisted, real-
time observation in the post-stroke period. First, we will examine the accuracy of SocialBit to detect the
social interaction time against direct observation in 200 patients (Aim 1). Second, we will determine the
association of social interaction time to social isolation and stroke outcomes at 3 months (Aim 2). Finally,
we will determine the medical factors associated with social interaction time (Aim 3). This study will
establish the key criteria of quantifying social interaction in stroke recovery research. The project will (a)
identify automatic and unobtrusive methods to measure social interaction, (b) determine key design and
outcome criteria for a future intervention trial, and (c) increase our understanding of underlying
mechanisms in social changes after stroke. In so doing, this study will address the public health priority
of building better behavioral modification strategies for patients with stroke.

## Key facts

- **NIH application ID:** 9973762
- **Project number:** 1R01HD099176-01A1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Amar Dhand
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $597,152
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9973762

## Citation

> US National Institutes of Health, RePORTER application 9973762, SocialBit: Establishing the accuracy of a wearable sensor to detect social interactions after stroke (1R01HD099176-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9973762. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*