MLwell lab

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.

While working on her Ph.D. in computer science, Maya recently graduated from her under grad studies in Mathematics.

The Israel Science Foundation has announced that it will support our research on "Explainable Learning on Graphs and Sets" for 3 years. We are very thankful and eager to work on this topic with this great support.

Ran Gilad-Bachrach will deliver a short talk as a part of the 1st Data-Government conference in Israel in the Reichman University,

Read the blog post of Atai Ambus on our new tool, dftest, for unit-testing data. This is our contribution to the data centric AI field.

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.

In a new technical report we introduce a new tool for auto-grading and auto-assessment to support the learning of computations thinking skills

Congratulations to Amnon Catav for successfully defending his Master's thesis

In a new technical report we discuss a version of the multi-armed bandit problem that is designed to delivering interventions to people and takes into account novelty effects.

Congratulations to Gon Shoham for successfully defending his Master's thesis

Congratulations to Roy Hirsch for successfully defending his Master's thesis

Congratulations to Omri Armstrong for successfully defending his Master's thesis