Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for Motor-Impaired Individuals via Latent Embedding Exploitation

TL;DR

This paper introduces a novel latent embedding exploitation mechanism within a few-shot continual learning framework to improve hand gesture recognition for motor-impaired individuals using wearable sensors, effectively handling out-of-distribution data with limited samples.

Contribution

The paper proposes the Latent Embedding Exploitation (LEE) mechanism that enhances few-shot continual learning for gesture recognition by leveraging diversified latent features and prior knowledge.

Findings

Achieved up to 69.3% accuracy with five samples per gesture.

Effectively captures variable gesture features with limited data.

Improves out-of-distribution gesture recognition performance.

Abstract

Hand gestures can provide a natural means of human-computer interaction and enable people who cannot speak to communicate efficiently. Existing hand gesture recognition methods heavily depend on pre-defined gestures, however, motor-impaired individuals require new gestures tailored to each individual's gesture motion and style. Gesture samples collected from different persons have distribution shifts due to their health conditions, the severity of the disability, motion patterns of the arms, etc. In this paper, we introduce the Latent Embedding Exploitation (LEE) mechanism in our replay-based Few-Shot Continual Learning (FSCL) framework that significantly improves the performance of fine-tuning a model for out-of-distribution data. Our method produces a diversified latent feature space by leveraging a preserved latent embedding known as gesture prior knowledge, along with intra-gesture divergence derived from two additional embeddings. Thus, the model can capture latent statistical structure in highly variable gestures with limited samples. We conduct an experimental evaluation using the SmartWatch Gesture and the Motion Gesture datasets. The proposed method results in an average test accuracy of 57.0%, 64.6%, and 69.3% by using one, three, and five samples for six different gestures. Our method helps motor-impaired persons leverage wearable devices, and their unique styles of movement can be learned and applied in human-computer interaction and social communication. Code is available at: https://github.com/riyadRafiq/wearable-latent-embedding-exploitation