A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors.

Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants' attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head).

A Hybrid Hierarchical Framework for Free Weight Exercise Recognition and Intensity Measurement with Accelerometer and ECG Data Fusion.

Accurate recognition and effective monitoring of physical activities (PA) in daily life is a goal of many healthcare fields. Existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we present a hybrid hierarchical framework that successfully combines two of the main specific-sensor-based PA methods into an effective hybrid solution for general weight exercise applications. The fusion solution separates free weight and non-free weight activities and then further classifies free weight exercises, whilst measuring quantities of repetitions and sets, thus providing a measure of intensity. By fusing accelerometer and electrocardiogram (ECG) data, a One Class Support Vector Machine (OC-SVM) and a Hidden Markov Model (HMM) are applied for exercise recognition and we use semantic inference for determining the intensity of the exercise. The results are based on data from 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/m^2; body fat: 20.5 ± 5.4), which shows good accuracy in exercise recognition and intensity measurement. This framework can be extended to support additional types of PA recognition in complex applications.

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.