Edmond Mitchell

Classification of Sporting Activities Using Smartphone Accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in todays society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach.

Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athletes activities in an outdoor training environment. We firstly present a system that automatically classifies a large range of training activities using the Discrete Wavelet Transform (DWT) in conjunction with a Random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subjects movement technique differed to the normative data in order to identify potential injury related factors. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments.

Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athletes activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subjects movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.

Breathing Feedback System with Wearable Textile Sensors

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician.

HeartHealth: A Cardiovascular Disease Home-based Rehabilitation System

Abstract The increasing pressure on medical institutions around the world requires health care professionals to be prescribing home- based exercise rehabilitation treatments to empower patients to self-monitor their rehabilitation journey. Home-based exercise rehabilitation has shown to be highly effective in treating conditions such as Cardiovascular Disease (CVD). However, adherence to home-based exercise rehabilitation remains low. Possible causes for this are that patients are not monitored, they cannot be con- fident that they are performing the exercise correctly or accurately and they receive no feedback. This paper proposes HeartHealth, a novel patient-centric gamified exercise rehabilitation platform that can help address the issue of adherence to these programmes. The key functionality is the ability to record the patient movements and compare them against the exercises that have been pre- scribed in order to return feedback to the patient and to the health care professional, as well. In order to synthesize a compact fully operational system able to work in real life scenarios, tools and services from FI-PPP projects, FIWARE 1 and FI-STAR 2, were exploited and a new FI-STAR component, Motion Evaluation Specific Enabler (SE), was designed and developed. The HeartHealth system brings together real-time cloud-based motion evaluation coupled with accurate low-cost motion capture, a personalised ex- ercise rehabilitation programme and an intuitive and fun serious game interface, designed specifically with a Cardiac Rehabilitation population in mind.

Automatically detecting asymmetric running using time and frequency domain features

Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: expensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Transform (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments.

Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.

HeartHealth: New Adventures in Serious Gaming

We present a novel, low-cost, interactive, exercise-based rehabilitation system. Our research involves the investigation and development of patient-centric, sensor-based rehabilitation games and surrounding technologies. HeartHealth is designed to provide a safe, personalised and fun exercise environment that could be deployed in any exercise based rehabilitation program. HeartHealth utilises a cloud-based patient information management system built on FIWARE Generic Enablers,and motion tracking coupled with our sophisticated motion comparison algorithms. Users can record customised exercises through a doctors interface and then play the rehabilitation game where they must perform a sequence of their exercises in order to complete the game scenario. Their exercises are monitored, recorded and compared by our Motion Evaluation software and real-time feedback is than given based on the users performance.