The Machine Learning for Health and Well-Being (MLwell) Lab is a research lab at the Bio-Medical Engineering department at Tel-Aviv University. Our vision is to create the technology to allow everyone and everywhere access to personalized medicine and precision psychology that is: (i) effective (ii) respects the biological, cultural and behavioral differences between people (iii) respects privacy and other ethical requirements (iv) affordable. Our mission is to improve the state in the art in machine learning algorithms for personalized medicine and precision psychology.
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision–recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
When interacting with humans, boredom/novelty should be considered when selecting content. In this paper, recently presented at AIstats we present a novel version of the multi-armed bandit problem that defines this problem. We present theoretical analysis of the problem and algorithms.
Congratulations to Amnon Catav for defending his thesis
Congratulations to Amnon Catav for successfully defending his Master's thesis
Congratulations to Gon Shoham for defending his thesis
Congratulations to Gon Shoham for successfully defending his Master's thesis
Congratulations to Roy Hirsch for defending his thesis
Congratulations to Roy Hirsch for successfully defending his Master's thesis
Congratulations to Omri Armstrong for defending his thesis
Congratulations to Omri Armstrong for successfully defending his Master's thesis