1 research outputs found
Identification of Persons and Several Demographic Features based on Motion Analysis of Various Daily Activities using Wearable Sensors
In recent years, there has been an increasing interest in using the capabilities of wearable sensors, including
accelerometers, gyroscopes and magnetometers, to recognize individuals while undertaking a set of normal daily
activities. The past few years have seen considerable research exploring person recognition using wearable sensing
devices due to its significance in different applications, including security and human-computer interaction
applications.
This thesis explores the identification of subjects and related multiple biometric demographic attributes based on the
motion data of normal daily activities gathered using wearable sensor devices. First, it studies the recognition of 18
subjects based on motion data of 20 daily living activities using six wearable sensors affixed to different body
locations. Next, it investigates the task of classifying various biometric demographic features: age, gender, height,
and weight based on motion data of various activities gathered using two types of accelerometers and one gyroscope
wearable sensors. Initially, different significant parameters that impact the subjects' recognition success rates are
investigated. These include studying the performance of the three sensor sources: accelerometer, gyroscope, and
magnetometer, and the impact of their combinations. Furthermore, the impact of the number of different sensors
mounted at different body positions and the best body position to mount sensors are also studied. Next, the analysis
also explored which activities are more suitable for subject recognition, and lastly, the recognition success rates and
mutual confusion among individuals. In addition, the impact of several fundamental factors on the classification
performance of different demographic features using motion data collected from three sensors is studied. Those
factors include the performance evaluation of feature-set extracted from both time and frequency domains, feature
selection, individual sensor sources and multiple sources.
The key findings are: (I) Features extracted from all three sensor sources provide the highest accuracy of subjects
recognition. (2) The recognition accuracy is affected by the body position and the number of sensors. Ankle, chest,
and thigh positions outperform other positions in terms of the recognition accuracy of subjects. There is a
depreciating association between the subject classification accuracy and the number of sensors used. (3) Sedentary
activities such as watching tv, texting on the phone, writing with a pen, and using pc produce higher classification
results and distinguish persons efficiently due to the absence of motion noise in the signal. (4) Identifiability is not
uniformly distributed across subjects. (5) According to the classification results of considered biometric features,
both full and selected features-set derived from all three sources of two accelerometers and a gyroscope sensor
provide the highest classification accuracy of all biometric features compared to features derived from individual
sensors sources or pairs of sensors together. (6) Under all configurations and for all biometric features classified; the
time-domain features examined always outperformed the frequency domain features. Combining the two sets led to
no increase in classification accuracy over time-domain alone