Andong Zhan

Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study

BACKGROUND Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinsons disease (PD) are lacking. METHODS Participants underwent baseline in-clinic assessments, including the Unified Parkinsons Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. RESULTS Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). CONCLUSION Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.

Accurate caloric expenditure of bicyclists using cellphones

Biking is one of the most efficient and environmentally friendly ways to control weight and commute. To precisely estimate caloric expenditure, bikers have to install a bike computer or use a smartphone connected to additional sensors such as heart rate monitors worn on their chest, or cadence sensors mounted on their bikes. However, these peripherals are still expensive and inconvenient for daily use. This work poses the following question: is it possible to use just a smartphone to reliably estimate cycling activity? We answer this question positively through a pocket sensing approach that can reliably measure cadence using the phones on-board accelerometer with less than 2% error. Our method estimates caloric expenditure through a model that takes as inputs GPS traces, the USGS elevation service, and the detailed road database from OpenStreetMap. The overall caloric estimation error is 60% smaller than other smartphone-based approaches. Finally, the smartphone can aggressively duty-cycle its GPS receiver, reducing energy consumption by 57%, without any degradation in the accuracy of caloric expenditure estimates. This is possible because we can recover the bikes route, even with fewer GPS location samples, using map information from the USGS and OpenStreetMap databases.

A closed-loop approach for improving the wellness of low-income elders at home using game consoles

As life expectancy in the industrialized world increases, so does the number of elders with chronic health conditions such as diabetes and congestive heart failure who require complex self-management routines. We present a motivational exercise gaming system whose goal is to increase the activity of elders with complex chronic conditions. Our gaming system, initially deployed unattended, showed discouraging results. Our second attempt addresses these shortcomings by coupling the gaming console with an application for presenting exercise results to remote clinicians and caregivers, and a smartphone- based application for collecting feedback and issuing alerts. HealthOS, a platform for developing healthcare applications, integrates all components of the applications.

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score

Importance Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. Objectives To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. Design, Setting, and Participants This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning–based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. Main Outcomes and Measures Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. Results The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease’s Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. Conclusions and Relevance Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Objectives: To develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU. Design: Prospective, observational study. Setting: Surgical ICU at an academic hospital. Patients: Three hundred sixty-two hours of sensor color and depth image data were recorded and curated into 109 segments, each containing 1,000 images, from eight patients. Interventions: None. Measurements and Main Results: Three Microsoft Kinect sensors (Microsoft, Beijing, China) were deployed in one ICU room to collect continuous patient mobility data. We developed software that automatically analyzes the sensor data to measure mobility and assign the highest level within a time period. To characterize the highest mobility level, a validated 11-point mobility scale was collapsed into four categories: nothing in bed, in-bed activity, out-of-bed activity, and walking. Of the 109 sensor segments, the noninvasive mobility sensor was developed using 26 of these from three ICU patients and validated on 83 remaining segments from five different patients. Three physicians annotated each segment for the highest mobility level. The weighted Kappa (&kgr;) statistic for agreement between automated noninvasive mobility sensor output versus manual physician annotation was 0.86 (95% CI, 0.72–1.00). Disagreement primarily occurred in the “nothing in bed” versus “in-bed activity” categories because “the sensor assessed movement continuously,” which was significantly more sensitive to motion than physician annotations using a discrete manual scale. Conclusions: Noninvasive mobility sensor is a novel and feasible method for automating evaluation of ICU patient mobility.

Poster: HealthOS: a platform for integrating and developing pervasive healthcare applications

1. THE HEALTHOS SYSTEM Recently, an increasing number of pervasive healthcare applications have been developed as a way to overcome the shortcomings of the traditional clinical infrastructure. However, the nature of these applications, both closed and vertically-integrated, hinders integration with the existing infrastructure, increases the development cost, and fails to provide a unified management interface. In response to these challenges, we propose HealthOS, a platform designed to develop pervasive healthcare applications. Figure 1 illustrates the target environment for HealthOS. As the figure suggests, HealthOS users can carry multiple healthcare-related devices in their living environments, each using their proprietary communication protocols and data formats. HealthOS collects, encrypts, and stores the data on either a local machine or in a secure cloud service. Upon request, HealthOS can translate the data into requested formats that different healthcare applications may require. From the perspective of an application developer, the attractiveness of the HealthOS platform lies in the need to implement only the analysis and representation logic of the application. We also envision the use of a HealthStore (similar to the Apple AppStore), in which pre-developed applications can be shared and reused. The various applications are handled using the unified management console in HealthOS. Furthermore, HealthOS can be adapted to mobile platforms. Achieving our vision of HealthOS, requires implementing several modules for each device. First, a module is necessary to communicate with each device and translate the custom data that it produces. We call this module a HealthOS driver. Secondly, the formats used to present the data to different stakeholders (e.g., family members, healthcare professionals, etc.) may differ. Thus, HealthOS uses a translator module used for converting a device’s proprietary data format to match well-defined, standardized medical data presentation formats used by major electronic medical record (EMR) systems [2, 6]. Both drivers and translators follow a component-based design in the sense that they provide and require well-defined interfaces for inter-module interaction and reuse. Given that data can successfully be collected and interpreted using HealthOS, application developers can focus solely on properly

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability.

Development of an Exergame for Urban-dwelling Older Adults With Functional Limitations: Results and Lessons Learned

Background: Falls at home are common and potentially fatal for disabled older adults. To address this problem, we created an academic–community partnership involving disabled, urban-dwelling older adults and their families, the housing authority, a Tai Chi master, and a university.Objectives: We conducted a pilot to assess safety, acceptability, and feasibility of a Wii-based exergame designed to increase disabled older adults’ strength and balance.Methods: A working prototype was developed and evaluated. Then, we piloted a refined version with 19 disabled urban-dwelling older adults.Results: The program was enjoyable, feasible, and acceptable. Participants described multiple functional improvements. Of the 16 who completed at least three gaming sessions, average balance score increased 25% and gait speed increased 19%.Conclusions: This pilot showed promising results for improving strength and balance in the home setting, and yielded valuable lessons about health technology development with community partners.