Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

Abstract

Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, Ceolini et al. Sensor Fusion Neuromorphic Benchmark with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

“…SLAYER with Loihi has also been used for tactile digit recognition by See et al [38] and for sensor fusion because of the ease of combining modalities in the spike domain. Ceolini et al [39] combined EMG and vision data in a gesture classification task, and Taunyazov et al [40] combined vision and tactile data in a grasping task.…”

“…Much work remains to be done on the spiking algorithm front, but steady progress is being made. Early efforts demonstrate digit recognition, fusion of visual and tactile perception [40], fusion of visual and EMG information [39], persistent attention and tracking [68], and online learning of gestures [102] using event-based sensors interfaced to Loihi.…”

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

“…In addition to conventional DL accelerators, there have been significant research efforts to utilize biologically plausible SNNs for learning and cognition [42]. Spiking neuromorphic processors have also been used for biomedical signal processing [43]- [45]. Below, we provide a brief introduction to SNNs, which will be discussed as a method complementary to DL accelerators for efficient biomedical signal processing later in this paper.…”

“…In Section III we will present a use-case of bio-signal processing where FPGA and memristive DNN accelerators are implemented and simulated. These are later compared to equivalent existing implementations 1 using DNN accelerators and neuromorphic processors from [45]. To perform comparisons, we use the same hand-gesture recognition task as in [45].…”

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

“…Indeed, the discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neuro-rehabilitation. For this purpose, we proposed in References [89,90] a framework that allows the integration of multi-sensory data to perform sensor fusion based on supervised learning. The framework was applied for the hand gestures recognition task with five hand gestures: Pinky (P), Elle (E), Yo (Y), Index (I) and Thumb (T).…”