Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing

MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones

Mobile sensing systems have made significant advances in tracking human behavior. However, the development of personalized mobile health feedback systems is still in its infancy. This paper introduces MyBehavior, a smartphone application that takes a novel approach to generate deeply personalized health feedback. It combines state-of-the-art behavior tracking with algorithms that are used in recommendation systems. MyBehavior automatically learns a user's physical activity and dietary behavior and strategically suggests changes to those behaviors for a healthier lifestyle. The system uses a sequential decision making algorithm, Multi-armed Bandit, to generate suggestions that maximize calorie loss and are easy for the user to adopt. In addition, the system takes into account user's preferences to encourage adoption using the pareto-frontier algorithm. In a 14-week study, results show statistically significant increases in physical activity and decreases in food calorie when using MyBehavior compared to a control condition

Vestibular contribution to path integration deficits in 'at-genetic-risk' for Alzheimer's disease.

Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers

Alzheimer's disease patients getting lost in the community: Is road network structure a significant risk factor?

Background: Getting lost is one of the earliest and most distressing symptoms seen in Alzheimer’s disease (AD). Despite being a prevalent problem in the community worldwide, very few studies have explored real-world environmental factors that may potentially contribute to patients getting lost. In this study, we aim to investigate whether road network structure plays a contributory role to AD patients getting lost in the community using retrospective and prospective data. Method: Retrospective data of police case records of a large sample of missing dementia patients in the Norfolk county (n = 210), over a three year period, was used first to test our aim. Here, for each missing patient location, we measured the road intersection density, intersection complexity, as well as orientation entropy at a 1 km radius buffer zone around these locations; these measures were then compared to that of a set of random but matched locations. We then collected data prospectively to test the performance of 18 community-dwelling patients with AD (aged 50-80 years) on their ability to find their way in their own neighbourhood using a novel ‘Detour Navigation Task’. Here, we will measure the road intersection density, complexity, and orientation entropy at the locations in their neighbourhood where patients exhibited spatial disorientation. Result: The 210 patients in the police data went missing from a total of 168 locations in the Norfolk county. Our results show that the locations patients went missing from had significantly increased road intersection density, complexity, and orientation entropy when compared to that of the matched locations. Meanwhile, the performance of the sample of 18 AD patients on the Detour Navigation Task will then be analysed to see whether the neighbourhood locations where the patients felt disoriented exhibited higher road intersection density, complexity, and orientation entropy when compared to locations where they did not feel disoriented. Conclusion: The results of this study will provide evidence for road network structure as being a significant factor contributing to AD patients getting lost. This can in future help to potentially identify patients at high risk for getting lost as well as inform safeguarding guidelines

Multilinear multitask learning

Many real world datasets occur or can be arranged into multi-modal structures. With such datasets, the tasks to be learnt can be referenced by multiple indices. Current multitask learning frameworks are not designed to account for the preservation of this information. We propose the use of multilinear algebra as a natural way to model such a set of related tasks. We present two learning methods; one is an adapted convex relaxation method used in the context of tensor completion. The second method is based on the Tucker decomposition and on alternating minimization. Experiments on synthetic and real data indicate that the multilinear approaches provide a significant improvement over other multitask learning methods. Overall our second approach yields the best performance in all datasets

Using GPS Tracking to Investigate Outdoor Navigation Patterns in Patients With Alzheimer Disease:Cross-sectional Study

Background: Spatial disorientation is one of the earliest and most distressing symptoms seen in patients with Alzheimer disease (AD) and can lead to them getting lost in the community. Although it is a prevalent problem worldwide and is associated with various negative consequences, very little is known about the extent to which outdoor navigation patterns of patients with AD explain why spatial disorientation occurs for them even in familiar surroundings. Objective: This study aims to understand the outdoor navigation patterns of patients with AD in different conditions (alone vs accompanied; disoriented vs not disoriented during the study) and investigate whether patients with AD experienced spatial disorientation when navigating through environments with a high outdoor landmark density and complex road network structure (road intersection density, intersection complexity, and orientation entropy). Methods: We investigated the outdoor navigation patterns of community-dwelling patients with AD (n=15) and age-matched healthy controls (n=18) over a 2-week period using GPS tracking and trajectory mining analytical techniques. Here, for the patients, the occurrence of any spatial disorientation behavior during this tracking period was recorded. We also used a spatial buffer methodology to capture the outdoor landmark density and features of the road network in the environments that the participants visited during the tracking period. Results: The patients with AD had outdoor navigation patterns similar to those of the controls when they were accompanied; however, when they were alone, they had significantly fewer outings per day (total outings: P < .001; day outings: P = .003; night outings: P < .001), lower time spent moving per outing (P = .001), lower total distance covered per outing (P = .009), lower walking distance per outing (P = .02), and lower mean distance from home per outing (P = .004). Our results did not identify any mobility risk factors for spatial disorientation. We also found that the environments visited by patients who experienced disorientation versus those who maintained their orientation during the tracking period did not significantly differ in outdoor landmark density (P = .60) or road network structure (road intersection density: P = .43; intersection complexity: P = .45; orientation entropy: P = .89). Conclusions: Our findings suggest that when alone, patients with AD restrict the spatial and temporal extent of their outdoor navigation in the community to successfully reduce their perceived risk of spatial disorientation. Implications of this work highlight the importance for future research to identify which of these individuals may be at an actual high risk for spatial disorientation as well as to explore the implementation of health care measures to help maintain a balance between patients' right to safety and autonomy when making outings alone in the community