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

Inferring transportation modes from GPS trajectories using a convolutional neural network

Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C: Emerging Technologie

Privacy-preserving human mobility and activity modelling

The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis. The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users. To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces