Recognition and repetition counting for local muscular endurance exercises in exercise-based rehabilitation: a comparative study using artificial Intelligence models

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g. their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance was better than the traditional approaches, for exercise recognition (overall F1-score of 97.18%) and repetition counting (±1 error among 90% observed sets). To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. Also, we make the INSIGHT-LME dataset publicly available to encourage further research

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Towards an automated weight lifting coach: introducing LIFT

The fitness device market is young and rapidly growing. More people than ever before take count of how many steps they walk, how many calories they burn, their heart rate over time, and even their quality of sleep. New, and as of yet, unreleased fitness devices have promised the next evolution of functionality with exercise technique analysis. These next generation of fitness devices have wrist and armband style form factors, which may not be optimal for barbell exercises such as back squat, bench press, and overhead press where a sensor on one arm may not provide the most relevant data about a lift. Barbell path analysis is a well-known visual tool to help diagnose weightlifting technique deficiencies, but requires a camera pointed at the athlete that is integrated with motion-tracking software. This camera set up is not available at most gyms, so this motivates the use of a small, unobtrusive sensor to obtain data about an athlete\u27s weightlifting technique. Researchers have shown that an accelerometer attached to a barbell while the athlete is lifting yields just as accurate acceleration information as a camera. The LIFT (Leveraging Information For Training) automated weight lifting coach attempts to implement a simple, unobtrusive system for analyzing and providing feedback on barbell weight lifting technique

A data fusion-based hybrid sensory system for older people’s daily activity recognition.

Population aged 60 and over is growing faster. Ageing-caused changes, such as physical or cognitive decline, could affect people’s quality of life, resulting in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) has become one of the most promising assistive technologies for older people’s daily life. Literature in HAR suggests that each sensor modality has its strengths and limitations and single sensor modalities may not cope with complex situations in practice. This research aims to design and implement a hybrid sensory HAR system to provide more comprehensive, practical and accurate surveillance for older people to assist them living independently. This reseach: 1) designs and develops a hybrid HAR system which provides a spatio- temporal surveillance system for older people by combining the wrist-worn sensors and the room-mounted ambient sensors (passive infrared); the wearable data are used to recognize the defined specific daily activities, and the ambient information is used to infer the occupant’s room-level daily routine; 2): proposes a unique and effective data fusion method to hybridize the two-source sensory data, in which the captured room-level location information from the ambient sensors is also utilized to trigger the sub classification models pretrained by room-assigned wearable data; 3): implements augmented features which are extracted from the attitude angles of the wearable device and explores the contribution of the new features to HAR; 4:) proposes a feature selection (FS) method in the view of kernel canonical correlation analysis (KCCA) to maximize the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the already selected features and the feature candidate, named mRMJR-KCCA; 5:) demonstrates all the proposed methods above with the ground-truth data collected from recruited participants in home settings. The proposed system has three function modes: 1) the pure wearable sensing mode (the whole classification model) which can identify all the defined specific daily activities together and function alone when the ambient sensing fails; 2) the pure ambient sensing mode which can deliver the occupant’s room-level daily routine without wearable sensing; and 3) the data fusion mode (room-based sub classification mode) which provides a more comprehensive and accurate surveillance HAR when both the wearable sensing and ambient sensing function properly. The research also applies the mutual information (MI)-based FS methods for feature selection, Support Vector Machine (SVM) and Random Forest (RF) for classification. The experimental results demonstrate that the proposed hybrid sensory system improves the recognition accuracy to 98.96% after applying data fusion using Random Forest (RF) classification and mRMJR-KCCA feature selection. Furthermore, the improved results are achieved with a much smaller number of features compared with the scenario of recognizing all the defined activities using wearable data alone. The research work conducted in the thesis is unique, which is not directly compared with others since there are few other similar existing works in terms of the proposed data fusion method and the introduced new feature set