Wearable devices and sensors have recently become a popular way to collect data,
especially in the health sciences. The use of sensors allows patients to be monitored over a …

Purpose: To determine 24-hour physical activity (PA) clusters in children 6–36 months of
age, factors associated with the clusters, and their agreement across time. Method: A …

Owing to advances in sensor technologies on wearable devices, it is feasible to measure
physical activity of an individual continuously over a long period. These devices afford …

Mixture of hidden Markov models for accelerometer data Page 1 The Annals of Applied
Statistics 2020, Vol. 14, No. 4, 1834–1855 https://doi.org/10.1214/20-AOAS1375 © Institute …

Several factors make clustering of functional data challenging, including the infinite-
dimensional space to which observations belong and the lack of a defined probability …

Background: Clustering, a class of unsupervised machine learning methods, has been
applied to physical activity data recorded by accelerometers to discover unique patterns of …

This study presents a new statistical method for clustering step data, a popular form of health
recording data easily obtained from wearable devices. As step data are high-dimensional …

1.1 Summary of my contributions...................... 7 1.1. 1 Presentation................................ 7 1.1. 2
Research Interests............................. 8 1.1. 3 Publications................................. 9 1.2 Outline of …

1. Introduction. Inadequate sleep and physical inactivity affect physical and mental well-
being while often exacerbating health problems. They are currently considered major risk …

This thesis studies clustering time-series data by suggesting a new similarity measure and
an optimization algorithm. To illustrate, we propose a new time-series clustering method …