Robert F. Dickerson

Monitoring body positions and movements during sleep using WISPs

Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform - active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.

Empath: a continuous remote emotional health monitoring system for depressive illness

Depression is a major health issue affecting over 21 million American adults that often goes untreated, and even when undergoing treatment it is hard to monitor the effectiveness of the treatment. To address these issues, we have created a real-time depression monitoring system for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well track progress managing a depressive illness. A cohesive set of integrated wireless sensors, a touch screen station, mobile device, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the clients current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well for patients in the management and tracking of their symptoms. We present data of a case study showing the value of the system, deployed over a period of two weeks in a home during a depressive episode. Larger scale studies are planned for the future.

MusicalHeart: a hearty way of listening to music

MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.

Auditeur: a mobile-cloud service platform for acoustic event detection on smartphones

Auditeur is a general-purpose, energy-efficient, and context-aware acoustic event detection platform for smartphones. It enables app developers to have their app register for and get notified on a wide variety of acoustic events. Auditeur is backed by a cloud service to store user contributed sound clips and to generate an energy-efficient and context-aware classification plan for the phone. When an acoustic event type has been registered, the smartphone instantiates the necessary acoustic processing modules and wires them together to execute the plan. The phone then captures, processes, and classifies acoustic events locally and efficiently. Our analysis on user-contributed empirical data shows that Auditeurs energy-aware acoustic feature selection algorithm is capable of increasing the device lifetime by 33.4%, sacrificing less than 2% of the maximum achievable accuracy. We implement seven apps with Auditeur, and deploy them in real-world scenarios to demonstrate that Auditeur is versatile, 11.04% - 441.42% less power hungry, and 10.71% - 13.86% more accurate in detecting acoustic events, compared to state-of-the-art techniques. We present a user study to demonstrate that novice programmers can implement the core logic of interesting apps with Auditeur in less than 30 minutes, using only 15 - 20 lines of Java code.

Stream feeds: an abstraction for the world wide sensor web

RFIDs, cell phones, and sensor nodes produce streams of sensor data that help computers monitor, react to, and affect the changing status of the physical world. Our goal in this paper is to allow these data streams to be first-class citizens on the World Wide Web. We present a new Web primitive called stream feeds that extend traditional XML feeds such as blogs and Podcasts to accommodate the large size, high frequency, and real-time nature of sensor streams. We demonstrate that our extensions improve the scalability and efficiency over the traditional model for Web feeds such as blogs and Podcasts, particularly when feeds are being used for in-network data fusion.

Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities

Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., Anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a residents normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly residents home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.

SEPTIMU: continuous in-situ human wellness monitoring and feedback using sensors embedded in earphones

A mobile phone, as a pervasive device, has great potential in human wellness monitoring. In this demo, we first present the design and implementation of our hardware - SEPTIMU. SEPTIMU consists of a small baseboard and a pair of tiny sensor boards embedded inside conventional earphones. The baseboard provides power conversion and data communication through the normal audio jack interface. The embedded sensor board is 1×1cm2 and integrates 3-axis accelerometer, gyroscope, thermometer, photodiode and microphone. Secondly, we evaluate SEPTIMU using a mobile application that continuously monitors body posture and provides feedback to the user.

Demo abstract: SEPTIMU — Continuous in-situ human wellness monitoring and feedback using sensors embedded in earphones

ABSTRACT A mobile phone, as a pervasive device, has great potential in human wellness monitoring. In this demo, we first present the design and implementation of our hardware — SEPTIMU. SEPTIMU consists of a small baseboard and a pair of tiny sensor boards embedded inside conventional earphones. The baseboard provides power conversion and data communication through the normal audio jack interface. The embedded sensor board is 1×1cm2 and integrates 3-axis accelerometer, gyroscope, thermometer, photodiode and microphone. Secondly, we evaluate SEPTIMU using a mobile application that continuously monitors body posture and provides feedback to the user.

sMFCC: exploiting sparseness in speech for fast acoustic feature extraction on mobile devices -- a feasibility study

Due to limited processing capability, contemporary smartphones cannot extract frequency domain acoustic features in real-time on the device when the sampling rate is high. We propose a solution to this problem which exploits the sparseness in speech to extract frequency domain acoustic features inside a smartphone in real-time, without requiring any support from a remote server even when the sampling rate is as high as 44.1 KHz. We perform an empirical study to quantify the sparseness in speech recorded on a smartphone and use it to obtain a highly accurate and sparse approximation of a widely used feature of speech called the Mel-Frequency Cepstral Coefficients (MFCC) efficiently. We name the new feature the sparse MFCC or sMFCC, in short. We experimentally determine the trade-offs between the approximation error and the expected speedup of sMFCC. We implement a simple spoken word recognition application using both MFCC and sMFCC features, show that sMFCC is expected to be up to 5.84 times faster and its accuracy is within 1.1% -- 3.9% of that of MFCC, and determine the conditions under which sMFCC runs in real-time.

Home wireless sensing system for monitoring nighttime agitation and incontinence in patients with Alzheimer's disease

Patients with Alzheimers Disease (AD) often experience urinary incontinence and agitation during sleep. There is some evidence that these phenomena are related, but the relationships (and the subsequent opportunity for caregiver intervention) has never been formally studied. In this work, the relationships among the times of occurrence of nighttime agitation, sleep continuity and duration, and urinary incontinence are identified for persons with AD by using innovative, non-invasive technology. Deployments in 12 homes demonstrate both the utility of the technical monitoring system and the discovered correlations between agitation and incontinence for these 12 AD patients. Implications of possible interventions are discussed. Lessons learned for technical, non-technical and health care implications are presented.