Edmond Mitchell | Dublin City University (original) (raw)
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Papers by Edmond Mitchell
Procedia Computer Science, 2015
Human motion analysis technologies have been widely employed to identify injury determining facto... more 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 t...
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recur... more 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.
2014 11th International Conference on Wearable and Implantable Body Sensor Networks, 2014
In this paper we present a framework that allows for the automatic identification of sporting act... more 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 today's 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.
IEEE Internet of Things Journal, 2015
ABSTRACT Motion analysis technologies have been widely used to monitor the potential for injury a... more ABSTRACT 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 athlete’s activities in real 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 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 is generated and used to determine if a subject’s 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.
2010 International Conference on Body Sensor Networks, 2010
ABSTRACT Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due... more ABSTRACT 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.
Breathing is an important factor in our well-being as it oxygenates the body, revitalizes organs,... more Breathing is an important factor in our well-being as it oxygenates the body, revitalizes organs, cells and tissues. It is a unique physiological system in that it is both voluntary and involuntary. By breathing in a slow, deep and regular manner, the heartbeat become smooth and regular, blood pressure normalizes, stress hormones drop, and muscles relax. Breathing techniques are important for athletes to improve performance and reduce anxiety during competitions. Patients with respiratory illnesses often tend to take shallow short ...
Wearable sensors provide a means of continuously monitoring a person in a natural setting. These ... more Wearable sensors provide a means of continuously monitoring a person in a natural setting. These sensors can “look in” by monitoring the wearer's health through physiological measurements and also by detecting their activities. Other sensors can be used to “look out” from the wearer into the environment through which he/she is moving, which may serve to detect any potential hazards or provide contextual information about the wearer's lifestyle. Wearable sensors can be harnessed to give immediate feedback to the wearer while also ...
Procedia Engineering, 2015
Procedia Computer Science, 2015
Human motion analysis technologies have been widely employed to identify injury determining facto... more 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 t...
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recur... more 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.
2014 11th International Conference on Wearable and Implantable Body Sensor Networks, 2014
In this paper we present a framework that allows for the automatic identification of sporting act... more 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 today's 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.
IEEE Internet of Things Journal, 2015
ABSTRACT Motion analysis technologies have been widely used to monitor the potential for injury a... more ABSTRACT 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 athlete’s activities in real 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 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 is generated and used to determine if a subject’s 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.
2010 International Conference on Body Sensor Networks, 2010
ABSTRACT Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due... more ABSTRACT 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.
Breathing is an important factor in our well-being as it oxygenates the body, revitalizes organs,... more Breathing is an important factor in our well-being as it oxygenates the body, revitalizes organs, cells and tissues. It is a unique physiological system in that it is both voluntary and involuntary. By breathing in a slow, deep and regular manner, the heartbeat become smooth and regular, blood pressure normalizes, stress hormones drop, and muscles relax. Breathing techniques are important for athletes to improve performance and reduce anxiety during competitions. Patients with respiratory illnesses often tend to take shallow short ...
Wearable sensors provide a means of continuously monitoring a person in a natural setting. These ... more Wearable sensors provide a means of continuously monitoring a person in a natural setting. These sensors can “look in” by monitoring the wearer's health through physiological measurements and also by detecting their activities. Other sensors can be used to “look out” from the wearer into the environment through which he/she is moving, which may serve to detect any potential hazards or provide contextual information about the wearer's lifestyle. Wearable sensors can be harnessed to give immediate feedback to the wearer while also ...
Procedia Engineering, 2015