Enhanced Lightweight CNN Using Joint Classification With Averaging Probability for sEMG-Based Subject-Independent Hand Gesture Recognition

Leelakittisin, Benjakarn; Trakulruangroj, Manatsanan; Sangnark, Soravitt; Wilaiprasitporn, Theerawit; Sudhawiyangkul, Thapanun

doi:10.1109/jsen.2023.3296649

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Most research so far has focused on classification [7]- [10], and regression studies are still a niche. Just like for classification, the ML revolution in sEMG regression has gathered momentum from the release of open-access datasets; two major examples are the Non-Invasive Adaptive Prosthetics Database 8 (NinaPro DB8) [11] for hand kinematics based on joint angles, and the High-densitY Surface Electromyogram Recordings (HYSER) dataset for multi-finger forces [12].…”

Section: Introductionmentioning

confidence: 99%

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Zanghieri,

Rapa,

Orlandi

et al. 2024

IEEE Sensors J.

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parallelism shortens latency Real-time embedded force estimation • accurate & all-fingers • robust cross-day High-Density sEMG Spike trains parallel ultra-low power MCU LIF Neurons 𝐝𝒙 𝐝𝒕 = 𝒙𝐝𝐫𝐢𝐯𝐞 − 𝒙 𝝉 deployment of selected LIFs' dynamics and fitted inference heuristic feature selection reduces computation workload fit of linear readout Abstract-Modeling hand kinematics and dynamics is a key goal for research on Human-Machine Interfaces, with surface electromyography (sEMG) being the most commonly used sensing modality. Though underresearched, sEMG regression-based modeling of hand movements and forces is promising for finer control than allowed by mapping to fixed gestures. We present an event-based sEMG encoding for multi-finger force estimation implemented on a microcontroller unit (MCU). We are the first to target the HYSER High-Density (HD)-sEMG dataset in multi-day conditions closest to a real scenario without a fixed force pattern. Our Mean Absolute Error of (8.4 ± 2.8)% of the Maximum Voluntary Contraction (MVC) is on par with State-of-the-Art (SoA) works on easier settings such as within-day, single-finger, or fixed-exercise. We deploy our solution for HYSER's hardest task on a parallel ultra-low power MCU, getting an energy consumption below 6.5 uJ per sample, 2.8× to 11× more energy-efficient than SoA single-core solutions, and a latency below 280 us per sample, shorter than HYSER's HD-sEMG sampling period, thus compatible with real-time operation on embedded devices.

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