Neurotremor: A wearable Supportive Device for Supporting Upper Limb Muscle Function
- URL: http://arxiv.org/abs/2510.19826v1
- Date: Thu, 02 Oct 2025 00:07:09 GMT
- Title: Neurotremor: A wearable Supportive Device for Supporting Upper Limb Muscle Function
- Authors: Aueaphum Aueawattthanaphisut, Thanyanee Srichaisak, Arissa Ieochai,
- Abstract summary: A sensor-fused wearable assistance prototype for upper-limb function (triceps brachii and extensor pollicis brevis) is presented.<n>The device integrates surface electromyography (sEMG), an inertial measurement unit (IMU), and flex/force sensors on an M5StickC plus an ESP32-S3 compute hub.<n>Control are bounded by a control-barrier-function safety envelope and delivered within game-based tasks with lightweight personalization.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A sensor-fused wearable assistance prototype for upper-limb function (triceps brachii and extensor pollicis brevis) is presented. The device integrates surface electromyography (sEMG), an inertial measurement unit (IMU), and flex/force sensors on an M5StickC plus an ESP32-S3 compute hub. Signals are band-pass and notch filtered; features (RMS, MAV, zero-crossings, and 4-12 Hz tremor-band power) are computed in 250 ms windows and fed to an INT8 TensorFlow Lite Micro model. Control commands are bounded by a control-barrier-function safety envelope and delivered within game-based tasks with lightweight personalization. In a pilot technical feasibility evaluation with healthy volunteers (n = 12) performing three ADL-oriented tasks, tremor prominence decreased (Delta TI = -0.092, 95% CI [-0.102, -0.079]), range of motion increased (+12.65%, 95% CI [+8.43, +13.89]), repetitions rose (+2.99 min^-1, 95% CI [+2.61, +3.35]), and the EMG median-frequency slope became less negative (Delta = +0.100 Hz/min, 95% CI [+0.083, +0.127]). The sensing-to-assist loop ran at 100 Hz with 8.7 ms median on-device latency, 100% session completion, and 0 device-related adverse events. These results demonstrate technical feasibility of embedded, sensor-fused assistance for upper-limb function; formal patient studies under IRB oversight are planned.
Related papers
- BIONIX: A Wireless, Low-Cost Prosthetic Arm with Dual-Signal EEG and EMG Control [0.0]
This project presents a low-cost, dual-mode neuro-muscular control system integrating electroencephalography (EEG) and electromyography (EMG)<n>EEG signals are acquired using the NeuroSky MindWave Mobile 2 and transmitted via ThinkGear Bluetooth packets to an ESP32 microcontroller.<n>A functional prototype was constructed using low-cost materials, with most expense attributed to the commercial EEG headset.
arXiv Detail & Related papers (2025-12-07T05:39:13Z) - Time-Series at the Edge: Tiny Separable CNNs for Wearable Gait Detection and Optimal Sensor Placement [3.7765281299298015]
We study on-device time-series analysis for gait detection in Parkinson's disease (PD) from short windows of triaxial acceleration, targeting resource-latency wearables and edge nodes.<n>We compare magnitude thresholding to three 1D CNNs for time-series analysis: a literature baseline (separable convolutions) and two ultra-light models - one purely separable and one with residual connections.
arXiv Detail & Related papers (2025-11-29T08:52:41Z) - Clinic-Oriented Feasibility of a Sensor-Fused Wearable for Upper-Limb Function [0.0]
Upper-limb weakness and tremor (4--12 Hz) limit activities of daily living (ADL) and reduce adherence to home rehabilitation.<n>To assess technical feasibility and clinician-relevant signals of a sensor-fused wearable targeting the triceps brachii and extensor pollicis brevis.
arXiv Detail & Related papers (2025-10-27T01:30:26Z) - DarwinWafer: A Wafer-Scale Neuromorphic Chip [43.876109856399886]
We present a hyperscale system-on-wafer that replaces off-chip interconnects with wafer-scale, high-density integration of 64 Darwin3 chiplets on a 300 mm silicon interposer.<n>A GALS NoC within each chiplet and an AER-based asynchronous wafer fabric with hierarchical time-step synchronization provide low-latency, coherent operation across the wafer.<n>DarwinWafer consumes 100 W and achieves 4.9 pJ/SOP, with 64 TSOPS peak throughput (0.64 TSOPS/W)
arXiv Detail & Related papers (2025-08-30T00:22:09Z) - eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures [35.94440287795584]
Building an eFlesh sensor requires only four components: a 3D printer, off-the-shelf magnets, a CAD model of the desired shape, and a magnetometer circuit board.<n>We provide an open-source design tool that converts convex OBJ/STL files into 3D-printable STLs for fabrication.<n>Our sensor characterization experiments demonstrate the capabilities of eFlesh: contact localization RMSE of 0.5 mm, and force prediction RMSE of 0.27 N for normal force and 0.12 N for shear force.
arXiv Detail & Related papers (2025-06-11T17:59:46Z) - Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller [1.0015171648915433]
A dense neural network is developed to detect arrhythmia on the Arduino Nano.<n>The model has a size of 1.267 KB, achieves an F1 score (macro-average) of 78.3% for classifying four types of arrhythmia, an accuracy rate of 96.38%, and requires 0.001314 MOps of floating-point operations (FLOPs)
arXiv Detail & Related papers (2025-04-04T15:30:02Z) - Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare [2.6066253940276347]
We propose a convolutional neural network (CNN) specifically adapted to predict breathing rates from ultra-wideband (UWB) channel impulse response (CIR) data.<n>We show it is feasible to deploy the algorithm on an nRF52840 system-on-chip requiring only 46 KB of memory and operating with an inference time of only 192 ms.
arXiv Detail & Related papers (2025-04-03T07:54:25Z) - Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems [37.35086075012511]
Beam tracking is an essential functionality of millimeter wave (mmWave, 30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems.
It operates by performing antenna sweeping at both base station (BS) and user equipment (UE) sides.
In absence of explicit signalling for the type of application at the air interface, in this paper, we propose a way to remotely detect it at the BS side based on the received signal strength pattern.
arXiv Detail & Related papers (2024-10-24T10:55:21Z) - Entropy-based machine learning model for diagnosis and monitoring of
Parkinson's Disease in smart IoT environment [0.0]
Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG.
With a fewer number of features, we achieved a maximum classification accuracy (ARKF) of 99.9%.
Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.
arXiv Detail & Related papers (2023-08-28T08:20:57Z) - Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's
Patients [7.612338614344926]
This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease.
Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck.
Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN)
arXiv Detail & Related papers (2022-11-27T17:05:39Z) - SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection
Classifier [68.8204255655161]
Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures.
For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts.
SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
arXiv Detail & Related papers (2021-10-01T23:01:20Z) - COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for
Automated Diagnosis and Severity Assessment of COVID-19 [39.57518533765393]
There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19.
We present an end-to-end multitask learning framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.
arXiv Detail & Related papers (2020-12-10T08:30:46Z) - Improving Efficiency in Large-Scale Decentralized Distributed Training [58.80224380923698]
We propose techniques to accelerate (A)D-PSGD based training by improving the spectral gap while minimizing the communication cost.
We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switchboard speech recognition task and the ImageNet computer vision task.
arXiv Detail & Related papers (2020-02-04T04:29:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.