Fahd Albinali

Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.

PURPOSE Previously, the National Health and Examination Survey measured physical activity with an accelerometer worn on the hip for 7 d but recently changed the location of the monitor to the wrist. This study compared estimates of physical activity intensity and type with an accelerometer on the hip versus the wrist. METHODS Healthy adults (n = 37) wore triaxial accelerometers (Wockets) on the hip and dominant wrist along with a portable metabolic unit to measure energy expenditure during 20 activities. Motion summary counts were created, and receiver operating characteristic (ROC) curves were then used to determine sedentary and activity intensity thresholds. Ambulatory activities were separated from other activities using the coefficient of variation of the counts. Mixed-model predictions were used to estimate activity intensity. RESULTS The ROC for determining sedentary behavior had greater sensitivity and specificity (71% and 96%) at the hip than at the wrist (53% and 76%), as did the ROC for moderate- to vigorous-intensity physical activity on the hip (70% and 83%) versus the wrist (30% and 69%). The ROC for the coefficient of variation associated with ambulation had a larger AUC at the hip compared to the wrist (0.83 and 0.74). The prediction model for activity energy expenditure resulted in an average difference of 0.55 ± 0.55 METs on the hip and 0.82 ± 0.93 METs on the wrist. CONCLUSIONS Methods frequently used for estimating activity energy expenditure and identifying activity intensity thresholds from an accelerometer on the hip generally do better than similar data from an accelerometer on the wrist. Accurately identifying sedentary behavior from a lack of wrist motion presents significant challenges.

Using wearable activity type detection to improve physical activity energy expenditure estimation

Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.

Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum

Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements in real-time using comfortable, miniature wireless sensors could advance autistic research and enable new intervention tools for the classroom that help children and their caregivers monitor and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using data collected from six children with ASD repeatedly observed in both laboratory and classroom settings. In the classroom, an overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. Challenges encountered when applying machine learning to this domain, as well as implications for the development of real-time classroom interventions and research tools, are discussed.

Automated Detection of Stereotypical Motor Movements

To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average, pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Precise and efficient recording of stereotypical motor movements could enable researchers and clinicians to systematically study what functional relations exist between these behaviors and specific antecedents and consequences. These measures could also facilitate efficacy studies of behavioral and pharmacologic interventions intended to replace or decrease the incidence or severity of stereotypical motor movements.

Design of a wearable physical activity monitoring system using mobile phones and accelerometers

This paper describes the motivation for, and overarching design of, an open-source hardware and software system to enable population-scale, longitudinal measurement of physical activity and sedentary behavior using common mobile phones. The “Wockets” data collection system permits researchers to collect raw motion data from participants who wear multiple small, comfortable sensors for 24 hours per day, including during sleep, and monitor data collection remotely.

Fast track article: Detecting stereotypical motor movements in the classroom using accelerometry and pattern recognition algorithms

Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements using comfortable, miniature wireless sensors could advance autism research and enable new intervention tools for the classroom that help children and their caregivers monitor, understand, and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using accelerometer data collected wirelessly from six children with ASD repeatedly observed by experts in real classroom settings. An overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. We also present pilot work in which non-experts use software on mobile phones to annotate stereotypical motor movements for classifier training. Preliminary results indicate that non-expert annotations for training can be as effective as expert annotations. Challenges encountered when applying machine learning to this domain, as well as implications for the development of real-time classroom interventions and research tools are discussed.

Correlating Sensors and Activities in an Intelligent Environment: A Logistic Regression Approach

An important problem in intelligent environments is how the system can identify and model users’ activities. This paper describes a new technique for identifying correlations between sensors and activities in an intelligent environment. Intelligent systems can then use these correlations to recognize the activities in a space. The proposed approach is motivated by the need for distinguishing the critical set of sensors that identifies a specific activity from others that do not. We compare several correlation techniques and show that logistic regression is a suitable solution. Finally, we describe our approach and report preliminary results.

Development of an eHealth System to Capture and Analyze Patient Sensor and Self-Report Data: Mixed-Methods Assessment of Potential Applications to Improve Cancer Care Delivery

Background Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers (COMPASS) is an electronic health (eHealth) platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective The first phase of this project aimed to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS to refine the system. Methods Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data were tabulated using thematic content analysis to identify the most clinically relevant objective and PRO domains. Results Thematic content analysis revealed that clinicians were most interested in monitoring vital statistics, symptoms, and functional status, including the physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8), and anxiety (n=7). Patients (2 in active treatment and 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would, however, like to provide some context to their data. Conclusions Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data need to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.