Wearable Sensing

This dataset contains electrocardiography, electromyography, accelerometer, gyroscope and magnetometer signals that were measured in different scenarios using wearable equipment on 13 subjects:

 - Weight movement in a horizontal position at an angle of approximately 45°.

- Vertical movement of the weights from the table to the floor and back.

- Moving the weights vertically from the table to the head and back.

- Rotational movement of the wrist while holding the weights with the arm extended, see Figure ~\ref{fig2}.

A poor posture is a common health issue for adolescents during their growth and development. A prolonged poor posture can lead to musculoskeletal pain and disorders, and may even affect adolescents' growth and development. However, it is time-consuming and subjective to assess the poor posture in adolescents. Thus it is crucial to obtain an accurate and rapid evaluation method for poor posture. 

This is an American Sign Language from 0-9 dataset based on an 8-electrode EIT on the arm. The excitation frequency is 50kHz, the current is 1.81mA, and the measurement circuit is based on a MAX30009 chip and a CH74HC4067 multiplexer.

The Human Activity Recognition (HAR) dataset comprises comprehensive data collected from various human activities including walking, running, sitting, standing, and jumping. The dataset is designed to facilitate research in the field of activity recognition using machine learning and deep learning techniques. Each activity is captured through multiple sensors providing detailed temporal and spatial data points, enabling robust analysis and model training.

Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.

The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).

All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.

The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).

The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).

Each file consists of a signals from one sigle ride.

The dataset Arabic Digit Sequential Electromyography (ADSE) is acquired for eight-lead sEMG data targeting sequential signals.

Highly accurate and lightweight automated movements quality assessment is essential for home rehabilitation patients. We propose a method for the assessment and quantification of movement quality based on the differential feature segments, the objective  is to emulate the expert evaluations of physicians as closely as possible with minimal data features. Employing the Gaussian mixture model (GMM) to divide continuous trend time-series data into fragment features, defined as feature segments.

Autistic people typically need methodical support as they explore and interact with their immediate surroundings and the objects associated with them, emphasising the importance of spatial knowledge and cognitive skills in improving and understanding their surroundings. The objective of this research paper is to present a conceptual and technical framework that could be of significant assistance in developing spatial ability and cognitive skills in autistic people.