Caregiver burden, stress, and depression resulting from dealing with agitation exhibited by some people with dementia (PWD) are the primary reasons cited for PWD transitioning from aging-in-place to a long-term care facility. Non-pharmacological interventions provided by caregivers can reduce the frequency and severity of agitation in PWD, but agitation can be unpredictable and influenced by the environment, and early signs of agitation often go undetected and can escalate to aggressive agitation that is more difficult to manage. As a result, most methods used to deal with agitation in dementia are reactive rather than proactive and are administered too late in an agitation escalation to be effective. A tool to predict agitation episodes and detect early stages of agitation would empower caregivers to intervene early and ultimately reduce agitation, thus reducing caregiver burden and extending aging-in-place and the associated quality-of-life and cost benefits.
MusicalHeart is a bio-feedback 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 the user is listening to music. The physiological information along with contextual information are then sent to a remote server, which provides dynamic music suggestions to assist 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, and the accuracy of activity level and context inference are on average 96.8% and 84.1%, respectively. We demonstrate the practicality of MusicalHeart by deploying it to 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 it's quality of recommendation improves over time.
Depression is a major health issue. Depression is often unrecognized and untreated. It also leads to many other medical problems because of reduced social interactions, less personal hygiene, increased alcohol use, and ignoring medication for current medical conditions. The main goal of this proposed work is to complete the implementation of a real-time depression monitoring product for the home. This product will run 24/7 and can detect the signs of depression early (in real-time) as well as monitor those already diagnosed with depression. It is multi-modal to increase accuracy and provide caregivers with accurate information to aide in their care giving and diagnosis. The same product can also be used to provide information about the effectiveness of any treatment. The end result will be improved quality of life and possible improvement of other medical conditions and problems caused by or related to the depression. The product produced is a cohesive set of integrated wireless sensors, a touch screen station, and associated software that delivers the above capabilities. Once the implementation is complete, it is expected that this product can be transferred to industry for use in homes and assisted living facilities.
Falls are dangerous for the aged population as they can adversely affect health. 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.
Individuals with walking disabilities as a result of cerebral palsy (CP), stroke, muscular dystrophy, brain injury, or many other conditions are often prescribed ankle foot orthoses (AFOs), as shown in Figure 1 (a), to aid in their walking. The large number of individuals with CP that use AFOs highlights the magnitude of the problem. United CP reports that an estimated 764,000 people in the United States have one or more symptoms of CP. It has been reported that more than 50% of these individuals are prescribed orthoses.
The prescription of AFOs usually has several treatment goals, including: (1) facilitating walking by controlling the position of the ankle and providing a base of support, (2) preventing contractures by putting muscles in a lengthened position and providing variable ranges of motion, and (3) preventing deformity by controlling the position of the foot/ankle. Previous research has found that modulation of ankle position and stiffness is a primary means for patients with CP to change performance range. Although laboratory tests – usually gait analysis of patients with CP wearing AFOs walking in clinics or camera-instrumented motion capture laboratories – provide some insight into the first goal, results in naturalistic settings are lacking and may be significantly different. Moreover, without more continuous, longitudinal measurement to help understand the long-term effects of AFO use, the efficacy of AFOs in achieving the second and third goals cannot be determined.
This project seeks a novel solution for assessing the efficacy of AFOs in a continuous and non-invasive manner. Ankle joint angle is identified as an important metric for this assessment. By using wireless inertial body sensor network (BSN) based motion capture sensor, we present a promising solution that the longitudinal monitoring of performance of AFOs is possible.
Essential Tremor is the most common form of involuntary movement disorder and is often a debilitating condition for those affected. In the most severe cases, long-term suppression is achieved by chronic thalamic stimulation. Known as deep brain stimulation (DBS), this stimulation uses electrodes implanted in the thalamus which has many parameters with a multitude of possible settings. Improving patient quality of life and tremor reduction requires determination of the optimal patient-specific settings through accurate and precise assessment of tremor severity during stimulator programming. In our work with the University of Virginia Department of Neurosurgery, we have introduced a technique to provide such assessment of Essential Tremor severity by applying the Teager Energy Function to data collected with a custom, wearable, inertial sensing technology (TEMPO 1.0) for continuous, non-invasive, objective measurement of movement disorder such as tremor.
Sleep monitoring systems are important to recognize sleeping disorders as early as possible for diagnosis and prompt treatment of disease. We propose a sleep monitoring system based on Intel WISPs (Wireless Identification and Sensing Platform). Our system does not require any additional action from the users outside their daily routines. We attach WISP tags to the bed mattress and collect accelerometer data reported by them. We analyze these data and infer body position of the users and movements they make while on the bed. We can then record entries and exits from the bed, and movements and body positions during sleep. Recently WISPs have been used to recognize various daily activities. Our system complements such activity recognition systems.
Forward Head Posture (FHP) is a common musculoskeletal disorder correlated with neck pain that affects a large percentage of the population. Though much medical research has suggested methods for diagnosis and detection of this posture, assessment has been limited due to reliance on individual in-clinic visits. In order to provide timely detection of FHP and real-time feedback for postural correction, continuous monitoring of craniovertebral (CV) angle is needed. This report introduces a solution for continuous, non-invasive assessment of CV angle for FHP detection using a wireless inertial body sensor platform. In addition, a real-time bio-feedback mechanism for postural correction and preventive medicine is presented. The results obtained are validated against a conventional in-clinic method, the Electronics Head Posture Instrument (EHPI), demonstrating the possibility of pervasive detection of FHP.
Gait speed is a particularly important parameter in geriatrics, as it is the number one predictor of mortality in adults over 65 years old, with differences of just a couple tenths of a meter per second predicting statistically significant outcome differences. The most common method for gait speed estimation in medical research and clinical practice is to simply use a stopwatch and a tape measure. This typically provides good accuracy but is insufficient for applications that require more continuous and longitudinal data, especially given that speed and many other gait parameters can vary significantly day-to-day and even hour-to-hour in geriatric and gait impaired populations. It is therefore highly desirable to be able to estimate gait speed using inertial BSNs and to do so with a resolution of better than 0.1 m/s.
Falls are associated with numerous physical and psychological morbidities, decreased quality of life, increased mortality, and high healthcare costs. Patients with end stage renal diseases (ESRD) on hemodialysis (HD) have high morbidity and mortality due to multiple causes, one of which is dramatically higher fall rates than the general population. The incidence of falls in HD patients has been found to be 1.18 falls/patient-year, in comparison to the people living in community who have incidence of falls ranging from 0.32-0.70 falls/person-year .
One of the challenges cited most by caregivers of persons with dementia is dealing with nighttime agitation and associated sleep disturbances. The proposed project seeks to reduce such disturbances by using innovative, non-invasive sensing technology to study the nighttime relationship between cognitive impairment, agitation, restlessness, and incontinence.