Research Direction:

Health care and business data analytics Health care data analytics This project aims to exploit and evaluate the potential of data analytics solutions for the development of

subject-specific ADL models using WiFi data. The system will subsequently identify abnormal activity

employing these models and provide appropriate alerts to a caregiver - such as a person entering the

kitchen to prepare a meal but that does not open the fridge/cabinets to get food; a person using or not

using a needed walker leading to increased fall risk; a person going to the washroom in the middle of the night and not returning to bed. Collaborators/ Support:

Publications:

• I. O. Joudeh, A.-M. Cretu, B. Wallace, R.A. Goubran, M. Allegue-Martinez, A. Alkhalid, and F. Knoefel, "Analyzing WiFi Channel State Information to Distinguish Sitting-Down and Standing-Up Activities", submitted to IEEE Trans. Instrum. & Meas, 2019, NEW. • I. O. Joudeh, A.-M. Cretu, B. Wallace, R. Goubran, A. Alkhalid, M. Allegue, and F. Knoefel, "WiFi Channel State Information-Based Recognition of Sitting-Down and Standing-Up Activities", IEEE Int. Conf. Medical Measurements and Applications, Turkey, 2019, NEW. • I. O. Joudeh, A.-M. Cretu, B. Wallace, R. Goubran, "Recognition of Sitting-down and Standing-up Activities using WiFi Channel State Information", Data Day 6.0, Carleton University, poster presentation, May 2019 (Research Poster Prize from the Statistical Society of Ottawa, first place). The design of multiple applications in human activity recognition, in areas such as healthcare, sports and safety, relies on wearable sensor

technologies. However, when making decisions based on the data

acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise among other experimental constrains, deteriorate data quality and model accuracy. To tackle these

issues, this paper presents a data-driven iterative learning framework to

classify human locomotion activities such as walk, stand, lie, and sit,

extracted from the Opportunity dataset. Data acquired by twelve 3D

acceleration sensors and seven inertial measurement units are initially de- noised using a two-stage consecutive filtering approach combining a band- pass Finite Impulse Response (FIR) and a wavelet filter. A series of

statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch,

yaw and the norm of the axial components. The novel approach aiming to

minimize the number of samples required to classify human locomotion

activities based on these features consists in an iterative extraction of the

best candidates to build a training dataset. Candidate training samples are selected when the Euclidean distance between an input data and its

cluster's centroid is larger than the mean plus the standard deviation of all Euclidean distances between all input data and their corresponding

clusters. The newly built dataset is then used to train an SVM multi-class

classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset. Publications:

• J. C. Davila, A.-M. Cretu and M. Zaremba, "Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework", MDPI Sensors, vol. 17, art. 1287, 2017, doi: 10.3390/s17061287. • J. C. Davila, A.-M. Cretu and M. Zaremba, "Iterative Learning for Human Activity Recognition from Wearable Sensor Data", 3rd Int. Electron. Conf. Sensors and Applications, 15-30. Nov. 2016, Sciforum El. Conf. Series, Vol. 3, 2016, S2002, doi: 10.3390/ecsa-3- S2002 (Best Paper Award). This project explores the capabilities of Kinect for human movement quantification for in-home physical

exercise monitoring. The analysis goes beyond the state-of-the-art by monitoring more joints and offering

more advanced reporting capabilities on the movement such as: the position and trajectory of each joint, the working envelope of each body member, the average velocity, and the fatigue of the user after an exercise

sequence. This data can be visualised and compared to a standard (e.g. a healthy user for rehabilitation

purposes) or an ideal performance (e.g. perfect sport pose for exercising) in order to give the user a measure on his/her own performance and incite his/her motivation to continue the training program. Such information can be used as well by a therapist or professional sports trainer to evaluate the progress of a patient or of a trainee. An adaptation is proposed as well for the posture estimation in the context of piano pedagogy. Collaborators/ Support: Publications: • H. Nematallah, S. Rajan and A.-M. Cretu, "Logistic Model Tree for Human Activity Recognition Using

Smartphone-Based Inertial Sensors", IEEE Sensors Conference, Montréal, 2019, NEW. • P. Payeur, G. M. Nascimento, J. Beacon, G. Comeau, A.-M. Cretu, V. D'Aoust, and M.-A.

Charpentier, "Human Gesture Quantification: an Evaluation Tool for Somatic Training and Piano

Performance", IEEE Symp. Haptic Audio-Visual Environments and Games, pp. 100-105, Dallas, Texas, US, Oct. 2014. • S. Gauthier and A.-M. Cretu, "Human Movement Quantification using Kinect for In-Home Physical Exercise Monitoring”, IEEE Int.

Conf. Computational Intelligence and Virtual Environments for Measurement Systems and Applications, pp. 6-11, Ottawa, May 2014. Business data analytics This project aims at network data analysis to define and capture behavior profiles of network components via machine learning for the detection of anomalies, specifically for the classification of security concerns. Collaborators/ Support: The long term objective of this research project is to attempt to establish reliable associations between traffic data and accident occurrence. These associations should help a proactive approach for accident management by an automatic detection of prone locations and conditions for accidents. Collaborators/ Support:

Publications:

• E. Reveron, and A.-M. Cretu, "A Framework for Collision Prediction Using Historical Accident Information and Real-time Sensor Data: A Case Study for the City of Ottawa", IEEE Int. Symp. Robotics and Sensor Environments, Ottawa, pp. 143-149, 2019, NEW. • N.P. Bobbili and A.-M. Cretu, "Adaptive Weighting with SMOTE for Learning from Imbalanced Datasets: A Case Study for Traffic Offence Prediction", IEEE Int. Conf. Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Ottawa, Canada, 2018. • A.-M. Cretu, A. Mageau-Pétrin and C. Hopgood, “Traffic Accident Modeling and Prediction”, Ottawa Police Services Research Project, Technical report, May 2016, 33 pages. • A.-M. Cretu, and A. Mageau-Pétrin, “Intelligence-led traffic enforcement: Data mining techniques for accident prediction”, Ottawa Police Services Research Project, Technical report, Jan. 2016, 159 slides. The objective of this project is to study the impact of the use of the avatars (or

profile images) for the detection of communities in social networks. Support: