Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory

Classification of Hand Gestures from Wearable IMUs using Deep Neural Network

IMUs are gaining significant importance in the field of hand gesture analysis, trajectory detection and kinematic functional study. An Inertial Measurement Unit (IMU) consists of tri-axial accelerometers and gyroscopes which can together be used for formation analysis. The paper presents a novel classification approach using a Deep Neural Network (DNN) for classifying hand gestures obtained from wearable IMU sensors. An optimization objective is set for the classifier in order to reduce correlation between the activities and fit the signal-set with best performance parameters. Training of the …

Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs

Advancements in gesture recognition algorithms have led to a significant growth in sign language translation. By making use of efficient intelligent models, signs can be recognized with precision. The proposed work presents a novel one-dimensional Convolutional Neural Network (CNN) array architecture for recognition of signs from the Indian sign language using signals recorded from a custom designed wearable IMU device. The IMU device makes use of tri-axial accelerometer and gyroscope. The signals recorded using the IMU device are segregated on the basis of their context, such as whether they …

用于HAR的左右穿戴传感器（IMU）一致性对比匹配

Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR

本文提出一种利用左右穿戴传感器对称性的自监督对比匹配方法，通过对比左右IMU数据提升表征质量，在Opportunity和MM-Fit数据集上显著优于监督和SimCLR基线，且在小数据量下仍有效。

ARC-Net: Activity Recognition Through Capsules

Human Activity Recognition (HAR) is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions t…

基于深度学习的帕金森步态地面反作用力估计：使用优化的IMU数据集

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data

本研究首次提出混合CNN-BiLSTM深度学习框架，用优化的IMU数据集估计帕金森患者垂直地面反作用力，仅需2个IMU即可实现稳健估计，为相关可穿戴系统提供实用方案。

Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition

We address the well-known wearable activity recognition problem of having to work with sensors that are non-optimal in terms of information they provide but have to be used due to wearability/usability concerns (e.g. the need to work with wrist-worn IMUs because they are embedded in most smart watches). To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system. The method transfers information from the source sensors to the latent representation of th…