One Residual with Three Reuses: A Wristband Front End for Gesture Sensing
- URL: http://arxiv.org/abs/2608.16542v1
- Date: Mon, 17 Aug 2026 13:16:25 GMT
- Title: One Residual with Three Reuses: A Wristband Front End for Gesture Sensing
- Abstract summary: Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end.<n>We present a design study of such a wristband front end in which a classifier wake-up gating, mmWave versus IMU routing, and innovation-based EKF measurement reweighting share a single on-chip residual generator.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power budget while pairing a micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) with a 60 GHz frequency-modulated continuous-wave (FMCW) radar to stay robust under occlusion and on-body drift. We present a design study of such a wristband front end in which classifier wake-up gating, mmWave versus IMU routing, and innovation-based EKF measurement reweighting share a single on-chip residual generator. The shared generator occupies 14.4 KB of program memory and 278 B of state and runs at 110K multiply-accumulates (MACs) per frame on an Ambiq Apollo4 Blue Plus class edge microcontroller unit (MCU). Across four public sensor data corpora (IPN Hand, SHREC 2021, MiliPoint 60 GHz FMCW radar, EAT-Radar) the front end reaches detection probability $P_D = 0.72/0.80$ at a 1% false-alarm rate, sustains a 47% classifier invocation energy reduction at 90% gesture detection recall, and lowers pose tracking root-mean-square error by $4.6\times$ under measurement bias drift relative to an adaptive Kalman with $R$-inflation baseline. Measured silicon power and on-body capture are deferred to follow-on hardware; the contribution here is a design study.
Related papers
- Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
arXiv Detail & Related papers (2026-07-13T14:03:26Z) - PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence [14.663411219312827]
PanLUNA is a compact FM that processes EEG, ECG, and PPG within a single shared encoder.<n>PanLUNA matches or exceeds models up to 57$times$ larger: 81.21% balanced accuracy on TUAB abnormal EEG detection.
arXiv Detail & Related papers (2026-04-05T22:35:42Z) - PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning [51.24484551729328]
We introduce PRISM, a single-pass policy based on a batch-global rejection-sampling variant of IMLE.<n> PRISM couples a temporal multisensory encoder with a linear-attention generator using a Performer architecture.<n>We demonstrate the efficacy of PRISM on a diverse real-world hardware suite, including loco-manipulation using a Unitree Go2 with a 7-DoF arm D1 and tabletop manipulation with a UR5 manipulator.
arXiv Detail & Related papers (2026-02-02T17:57:37Z) - Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection [44.78575994732947]
WRCFormer is a novel 3D object detection framework that fuses raw radar cubes with camera inputs via multi-view representations of the decoupled radar cube.<n>WRCFormer achieves state-of-the-art performance on the K-Radar benchmarks, surpassing the best model by approximately 2.4% in all scenarios.
arXiv Detail & Related papers (2025-12-28T15:32:17Z) - M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction [45.65814462778685]
M4Human is the current largest-scale (661K-frame) multimodal benchmark, featuring high-resolution mmWave radar, RGB, and depth data.<n>M4Human includes high-quality motion capture (MoCap) annotations with 3D meshes and global trajectories.<n>We establish benchmarks on both RT and RPC modalities, as well as multimodal fusion with RGB-D modalities.
arXiv Detail & Related papers (2025-12-13T16:08:59Z) - mmPred: Radar-based Human Motion Prediction in the Dark [43.00006337997152]
Existing Human Motion Prediction methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns.<n>This work introduces radar as a novel sensing modality for HMP, for the first time.<n>We propose mmPred, the first diffusion-based framework tailored for radar-based HMP.
arXiv Detail & Related papers (2025-11-29T06:26:55Z) - UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach [0.6236828594667636]
This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems.<n>Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar.
arXiv Detail & Related papers (2025-07-14T11:45:57Z) - A novel fast short-time root music method for vibration monitoring of high-speed spindles [5.048369760845484]
We present a fast Short-Time Root-MUMU (fSTrM) that exploits FFT-accelerated Lanczos bidiagonalization to reduce computational complexity.<n>We show that fSTrM achieves 1.2 Hz frequency resolution (vs. 12.5 Hz), 93% detection rate at $-$5 dB SNR and quantifies defect severity through harmonic content analysis.
arXiv Detail & Related papers (2025-06-21T05:35:38Z) - 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) - Ultra-low Power Deep Learning-based Monocular Relative Localization
Onboard Nano-quadrotors [64.68349896377629]
This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones.
To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations.
Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
arXiv Detail & Related papers (2023-03-03T14:14:08Z) - Dynamical low-noise microwave source for cold-atom experiments [37.69303106863453]
We present a low-phase-noise microwave source with two independently controllable output paths.
The presented microwave source combines two commercially available frequency synthesizers.
We demonstrate a low integrated phase noise of $480,mu$rad in the range of $10,$Hz to $100,$kHz.
arXiv Detail & Related papers (2020-03-24T17:27:21Z)
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.