Mark A. Hanson

Body Area Sensor Networks: Challenges and Opportunities

Body area sensors can enable novel applications in and beyond healthcare, but research must address obstacles such as size, cost, compatibility, and perceived value before networks that use such sensors can become widespread.

Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.

TEMPO 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment

This work presents TEMPO (Technology-Enabled Medical Precision Observation) 3.1, a third generation body area sensor platform that accurately and precisely captures, processes, and wirelessly transmits six-degrees-of-freedom inertial data in a wearable, non-invasive form factor. TEMPO 3.1 is designed to be usable to both the wearer and researcher, thereby enabling motion capture applications in body area sensor networks (BASNs). A complete system is designed and developed that includes the following: (1) enabling technologies and hardware design of TEMPO 3.1, (2) a custom real-time operating system (TEMPOS) that manages all aspects of signal acquisition, signal processing, data management, peripheral control, and wireless communication on a TEMPO node, and (3) a custom case design. The system is evaluated and compared to existing BASN hardware platforms. TEMPO 3.1 creates new opportunities for wearable, continuous monitoring applications and extends the research space of current efforts.

On-Body Inertial Sensing and Signal Processing for Clinical Assessment of Tremor

Tremor, the most common form of movement disorder, is an often debilitating condition that adversely affects an individuals ability to maintain functional independence. Efforts to study, diagnose, and treat such movement disorders are complicated by a dearth of quantitative, precise, or accurate methods for motion data collection and assessment. To address this deficiency, this paper provides two contributions: 1) the design of a body-area inertial sensing system and 2) the evaluation of postcapture, on-body signal-processing algorithms that transform sensed inertial data into clinically significant information pertaining to tremor symmetry. For the former, we present our technology that meets requirements for wearability, fidelity, battery life, and interoperability. For the latter, we demonstrate the efficacy of using filter-bank analysis and cross correlation to interpret tremor frequency and energy. We extend the previous work by presenting a wireless body-area inertial sensing technology and a method to reduce, by up to 30 times, the computational demands of cross correlation on such a resource-constrained technology. These efforts lay the foundation for real-time, on-body assessment of tremor as well as more intelligent and energy-efficient data transmission and storage decisions.

Neural Network Gait Classification for On-Body Inertial Sensors

Clinicians have determined that continuous ambulatory monitoring provides significant preventative and diagnostic benefit, especially to the aged population. In this paper we describe gait classification techniques based on data obtained using a new body area sensor network platform named TEMPO 3. The platform and its supporting infrastructure enable six-degrees-of-freedom inertial sensing, signal processing, and wireless transmission. The proposed signal processing includes data normalization to improve robustness, feature extraction optimized for classification, and wavelet pre-processing. The effectiveness of the platform is validated by implementing a binary classifier between shuffle and normal gait. Artificial neural networks and classifiers based on the Cerebellar Model Articulation Controller were tested and yielded classification accuracies (68%-98%) comparable to previous efforts that required more restrictive or intrusive apparatus. These results suggest a viable path to resource-constrained, on-body gait classification.

Body-coupled communication for body sensor networks

Body sensor networks (BSNs) offer a wealth of opportunities for precise, accurate, continuous, and non-invasive sensing of physiological phenomena, but their unique operating environment, the body-area, poses unique technical challenges. Popular communications solutions that utilize 2.4 GHz radio transmission suffer from significant and highly variable path loss in this setting. To compensate for such loss, radio transceivers often transmit at power levels at or above 1 mW -- a reality that limits battery life. We propose the use of body-coupled communication to address this issue, as it presents several distinct advantages over existing solutions, namely: reduced power consumption, minimal interference, and increased privacy. In this paper, we demonstrate a 23 MHz body-coupled channel that supports reliable data transfer with an average received power of 30 dBm over a 2.4 GHz radio frequency link. This scheme reduces power needed for transmission and increases battery life by up to 100%, while maintaining a favorable environment for application-specific quality of service requirements. Finally, we propose a system-level hardware architecture and explore its implications on BSN infrastructure.

Continuous, non-invasive assessment of agitation in dementia using inertial body sensors

Agitated behavior is one of the most frequent reasons that patients with dementia are placed in long-term care settings. These behaviors are indicators of distress and are associated with increased risk of injury to the patients and their caregivers. This study aims to explore the ability of a custom inertial wireless body sensor network (BSN) to objectively detect and quantify agitation, validating against currently accepted subjective clinical measures -- the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) -- within the nursing home setting. The ultimate goal is to enable continuous, real-time monitoring of physical agitation in any location over an extended period. Continuous, longitudinal assessment facilitates timely response to agitation events in order to minimize patient distress and risk for injury, to more appropriately titrate pharmacotherapy, and to enable staff (or caregivers) to successfully intervene. Six patients identified as being at high risk for agitated behaviors were enrolled in this pilot study. Patients underwent a series of the above validated tests of memory and agitation. The BSN nodes were applied at three sites on body for three hours while behaviors were annotated simultaneously. This process was subsequently repeated twice for each enrolled subject. The BSN data was then processed using Teager energy analysis, which an earlier study suggested was a promising method for extracting jerky and repetitive movements from inertial data. Results based on construct validity testing for agitation (CMAI) and aggression (ABS) were promising and suggest that additional study with larger sample sizes is warranted.

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.

A Wearable Inertial Sensing Technology for Clinical Assessment of Tremor

TEMPO (Technology-Enabled Medical Precision Observation) 1.0 is a novel, first-generation, wearable data collection and analysis platform for assessment of a variety of human movement disorders, including tremor. It enables quantitative, objective, and continuous measurement of movement with minimal invasiveness and inconvenience to the patient and clinician, respectively. This system meets requirements for wearability, data storage, sampling rate, number of sensors, interface methods, and form factor, which are necessary for applications on person. In addition to the design and development of a basic data acquisition device, various circuits and systems were engineered to interface wearable, triaxial MEMS inertial sensors. Furthermore, custom data analysis software that processes datasets collected from the device and sensors, was created, and has demonstrated clinical utility in the analysis of tremor. Data processing techniques include a unique filtering scheme and a novel application of cross-correlation. The analysis was conducted pre- and post-operatively, in conjunction with the University of Virginias Department of Neurosurgery, for a study of deep brain stimulation efficacy. This paper presents the engineering of and experimental results obtained with TEMPO 1.0 technology in the clinical assessment of tremor.

Application-Focused Energy-Fidelity Scalability for Wireless Motion-Based Health Assessment

Energy-fidelity trade-offs are central to the performance of many technologies, but they are essential in wireless body area sensor networks (BASNs) due to severe energy and processing constraints and the critical nature of certain healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques, capable of high compression ratios, can incur a reduction in application fidelity. In order to maximize system performance, these trade-offs must be considered at runtime due to the dynamic nature of BASN applications, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated trade-offs can be made dynamically. This article presents a data rate scalability framework within a motion-based health application context which demonstrates the design of efficient and efficacious wireless health systems.