Joint Angle Estimation with Customized Wristband Based on Online Incremental Learning
- URL: http://arxiv.org/abs/2605.29771v1
- Date: Thu, 28 May 2026 11:15:48 GMT
- Title: Joint Angle Estimation with Customized Wristband Based on Online Incremental Learning
- Abstract summary: This study proposes a system for estimating the angle of the wrist joint using a customized wristband based on an online incremental learning approach.<n>The results indicate that the sensors exhibit good performance under strain variations.
- Score: 13.41427943923829
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intelligent wearable technology plays an increasingly important role in human-computer interaction, motion, and health monitoring. To ensure comfort and practicality of use, one common form for motion monitoring is to utilize soft wearable sensors. However, many research applications regarding wearable sensors are simplistic and difficult to adapt to different situations. This study proposes a system for estimating the angle of the wrist joint using a customized wristband based on an online incremental learning approach. It is a two-stage estimation method: the first stage updates the model based on the wearer's wrist movement characteristics using online learning, integrating real-time data from an IMU as ground truth. The second stage utilizes the updated model for estimation of wrist joint angle solely with the wristband. In other words, model training is completed during data acquisition, allowing the trained model to be used for subsequent angle estimation. This method offers advantages in adapting to data drift caused by variations in different testing configurations, such as the left and right wrists of the same subject, deviations in the wearing position on the same wrist, and even differences among various subjects. The results indicate that the sensors exhibit good performance under strain variations, and the wrist joint trajectory estimation of the proposed system has an approximate error of 15 degree in different scenarios.
Related papers
- Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands [99.00248904465089]
Dexterous hands differ in finger structure, contact surfaces, and sensor layouts.<n>We present Bench2Dex, a simulation benchmark for visuo-tactile bimanual manipulation across 12 dexterous hands.
arXiv Detail & Related papers (2026-09-14T15:22:59Z) - HandSplatter: Automated Digital Goniometry from Neural Rendering [9.424505965573307]
Manual goniometry is labor-intensive and suffers from inconsistent inter-rater reliability.<n>We present a novel pipeline for 3-D hand joint location and pose estimation using neural rendering.
arXiv Detail & Related papers (2026-08-10T15:35:20Z) - Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model [3.003700133703872]
We study whether a deep learning model trained on hip-worn accelerometer data can transfer to wrist-worn accelerometer data for sitting versus non-sitting classification.<n>We use CHAP, a CNN-BiLSTM model originally developed for hip accelerometers, and evaluate its zero-shot performance on wrist data.
arXiv Detail & Related papers (2026-08-03T23:23:31Z) - Toward Personalized Darts Training: A Data-Driven Framework Based on Skeleton-Based Biomechanical Analysis and Motion Modeling [8.715408426200264]
This paper presents a data-driven dart training assistance system.<n>The system creates a closed-loop framework spanning motion capture, feature modeling, and personalized feedback.<n>It can detect poor trunk stability, abnormal elbow displacement, and imbalanced velocity control, then provide targeted recommendations.
arXiv Detail & Related papers (2026-04-01T16:51:30Z) - Diffusion-based Inverse Model of a Distributed Tactile Sensor for Object Pose Estimation [45.95486121114164]
We learn an inverse tactile sensor model using denoising diffusion to efficiently leverage tactile data for pose estimation.<n>The model is conditioned on tactile observations from a distributed tactile sensor and trained in simulation using a geometric sensor model.<n>For online pose estimation, we integrate the inverse model with a particle filter through a proposal scheme.
arXiv Detail & Related papers (2026-01-19T17:36:28Z) - PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring [0.9668407688201359]
PersonaDrift is a benchmark designed to evaluate machine learning and statistical methods for detecting progressive changes in daily communication.<n>The benchmark focuses on two forms of longitudinal change that caregivers highlighted as particularly salient: flattened sentiment and off-topic replies.<n>Preliminary results show that flattened sentiment can often be detected with simple statistical models in users with low baseline variability.
arXiv Detail & Related papers (2025-11-20T15:15:00Z) - Smooth-Distill: A Self-distillation Framework for Multitask Learning with Wearable Sensor Data [0.0]
This paper introduces Smooth-Distill, a novel self-distillation framework designed to simultaneously perform human activity recognition (HAR) and sensor placement detection.<n>Unlike conventional distillation methods that require separate teacher and student models, the proposed framework utilizes a smoothed, historical version of the model itself as the teacher.<n> Experimental results show that Smooth-Distill consistently outperforms alternative approaches across different evaluation scenarios.
arXiv Detail & Related papers (2025-06-27T06:51:51Z) - Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations.
In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
arXiv Detail & Related papers (2023-08-31T05:05:53Z) - Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing
Things [82.15959827765325]
We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL)
We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability.
Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
arXiv Detail & Related papers (2022-07-14T10:04:18Z) - Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator.
A cost-sensitive active learning approach is used to select optimal joint configurations.
The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
arXiv Detail & Related papers (2021-01-26T16:01:02Z) - Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor
Setups [68.8204255655161]
We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras.
The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
arXiv Detail & Related papers (2021-01-12T12:02:26Z) - SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
We present SensiX, a personal edge platform that stays between sensor data and sensing models.
We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems.
Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
arXiv Detail & Related papers (2020-12-04T23:06:56Z)
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.