Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm-Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)-to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual\u27s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident\u27s cognitive health diagnosis, with an accuracy of 0.84

Parallel Knowledge Discovery from Large Complex Databases

NASA is focusing on grand challenge problems in Earth and space sciences. Within these areas of science, new instrumentation will be providing scientists with unprecedented amounts of unprocessed data. Our goal is to design and implement a system that takes raw data as input and efficiently discovers interesting concepts that can target areas for further investigation and can be used to compress the data. Our approach will provide an intelligent parallel data analysis system

CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA. Code is available at: https://github.com/floft/caldaComment: Under review at IEEE Transactions on Pattern Analysis and Machine Intelligenc

Using continuous sensor data to formalize a model of in-home activity patterns

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients

Acute rotator cuff tendinopathy: does ice, low load isometric exercise, or a combination of the two produce an analgaesic effect?

This document is the Accepted Manuscript version of the following article: Parle PJ, Riddiford-Harland DL, Howitt CD, et al. 'Acute rotator cuff tendinopathy: does ice, low load isometric exercise, or a combination of the two produce an analgaesic effect?.' Br J Sports Med 2017;51:208-209, doi: http://dx.doi.org/10.1136/bjsports-2016-096107.Rotator cuff tendinopathies are the most commonly diagnosed musculoskeletal shoulder conditions and are associated with pain, weakness and loss of function.1 Tendon swelling may be associated with tendinopathy and may result from acute overload.2–3 An increase in tendon cells (tenocytes) and upregulation of large molecular weight proteoglycans, such as aggrecan, may increase tendon water content.2 There is uncertainty as to whether the swelling is related to the pain or is instead an observed but unrelated phenomenon. Weakness detected clinically may be due to pain inhibition.4–5 Early treatment of acute rotator cuff tendinopathy involves patient education and relative rest, and may include non-steroidal anti-inflammatory drugs (NSAIDs) to reduce pain, swelling and inflammation. Subacromial corticosteroid injections are also used to achieve the same purpose. These techniques show low to moderate evidence of reducing short-term pain but they do not improve function.6 The medications have side effects such as gastrointestinal tract complaints,7 and corticosteroids may damage tendon tissue.8 Identifying alternative ways to control pain and inflammation may be warranted. Two clinical procedures to manage RC tendinopathy include ice wraps and isometric exercise, however, there are no empirical data supporting their use. This pilot study, conducted at the Illawarra Sports Medicine Clinic, NSW, Australia, was designed to test (1) the short term analgaesic effect of these interventions and (2) the feasibility of a larger clinical trial for adults diagnosed with acute rotator cuff tendinopathy (<12 weeks).Peer reviewe