A real-world study of wearable sensors in Parkinson's disease.

Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.

GEORGE®: A Pilot Study of a Smartphone Application for Huntington's Disease.

BACKGROUND: Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD. OBJECTIVE: Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home. METHODS: We developed GEORGE®, an Android smartphone application for HD which assesses voice, chorea, balance, gait, and finger tapping speed. We then conducted an observational pilot study of individuals with manifest HD, prodromal HD, and without a movement disorder. In clinic, participants performed standard clinical assessments and a battery of active tasks in GEORGE. At home, participants were instructed to complete the activities thrice daily for one month. Sensor data were used to measure chorea, tap rate, and step count. Audio data was not analyzed. RESULTS: Twenty-three participants (8 manifest HD, 5 prodromal HD, 10 controls) enrolled, and all but one completed the study. On average, participants used the application 2.1 times daily. We observed a significant difference in chorea score (HD: 19.5; prodromal HD: 4.5, p = 0.007; controls: 4.3, p = 0.001) and tap rate (HD: 2.5 taps/s; prodromal HD: 8.9 taps/s, p = 0.001; controls: 8.1 taps/s, p = 0.001) between individuals with and without manifest HD. Tap rate correlated strongly with the traditional UHDRS finger tapping score (left hand: r = -0.82, p = 0.022; right hand: r = -0.79, p = 0.03). CONCLUSION: GEORGE is an acceptable and effective tool to differentiate individuals with and without manifest HD and measure key disease features. Refinement of the application's interface and activities will improve its usability and sensitivity and, ideally, make it useful for clinical care and research.

Dual Modulated QR Codes for Proximal Privacy and Security.

The ubiquitous presence of surveillance cameras severely compromises the security of private information (e.g. passwords) entered via a conventional keyboard interface in public places. We address this problem by proposing dual modulated QR (DMQR) codes, a novel QR code extension via which users can securely communicate private information in public places using their smartphones and a camera interface. Dual modulated QR codes use the same synchronization patterns and module geometry as conventional monochrome QR codes. Within each module, primary data is embedded using intensity modulation compatible with conventional QR code decoding. Specifically, depending on the bit to be embedded, a module is either left white or an elliptical black dot is placed within it. Additionally, for each module containing an elliptical dot, secondary data is embedded by orientation modulation; that is, by using different orientations for the elliptical dots. Because the orientation of the elliptical dots can only be reliably assessed when the barcodes are captured from a close distance, the secondary data provides "proximal privacy" and can be effectively used to communicate private information securely in public settings. Tests conducted using several alternative parameter settings demonstrate that the proposed DMQR codes are effective in meeting their objective- the secondary data can be accurately decoded for short capture distances (6 in.) but cannot be recovered from images captured over long distances (>12 in.). Furthermore, the proximal privacy can be adapted to application needs by varying the eccentricity of the elliptical dots used.

Deep Phenotyping of Parkinson's Disease.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

A Longitudinal Wearable Sensor Study in Huntington's Disease.

BACKGROUND: Most current measures of Huntington's disease (HD) motor symptoms are subjective, categorical, and limited to in-person visits. Wearable sensors enable objective, frequent, and remote data collection in real-world settings. However, longitudinal sensor studies in HD are lacking. OBJECTIVE: To measure motor symptoms of HD using wearable sensors in a longitudinal study. METHODS: Participants with HD, prodromal HD, and without a movement disorder wore five accelerometers, one on each limb and on the trunk, at up to four clinic visits over one year. After each visit, participants wore the sensors at home for two days. Based on the accelerometer data from the trunk, we calculated a "truncal Chorea Index" for periods when the participant was sitting. We also measured gait parameters and activity profiles. To measure group differences, track progression, and observe individual variability, statistical analysis of the data was conducted using a linear mixed-effects model. RESULTS: Fifteen individuals with HD, five with prodromal HD, and 19 controls were enrolled. The average truncal Chorea Index was higher in individuals with HD (26.6, p < 0.001) than in controls (15.6). For participants with HD, the truncal Chorea Index showed a high intra-day variability but minimal change over 12 months. Individuals with HD walked less (HD = 3818, prodromal HD = 6957, controls = 5514 steps/day) and took longer duration steps (HD = 0.97, prodromal HD = 0.78, controls = 0.85 seconds/step) than the other groups. Individuals with HD spent over half their day lying down (HD = 51.1%, prodromal HD = 38.0%, controls = 37.1%). CONCLUSIONS: A novel truncal Chorea Index can assess truncal chorea at home, finds substantial variability, and suggests that truncal chorea may be present in prodromal HD. Individuals with HD walk less and slower and spend more time lying down than controls. These findings require additional investigation, could inform clinical care, and could be used to evaluate new therapies.

A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective.

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

Multiple Wearable Sensors in Parkinson and Huntington Disease Individuals: A Pilot Study in Clinic and at Home.

BACKGROUND: Clinician rating scales and patient-reported outcomes are the principal means of assessing motor symptoms in Parkinson disease and Huntington disease. However, these assessments are subjective and generally limited to episodic in-person visits. Wearable sensors can objectively and continuously measure motor features and could be valuable in clinical research and care. METHODS: We recruited participants with Parkinson disease, Huntington disease, and prodromal Huntington disease (individuals who carry the genetic marker but do not yet exhibit symptoms of the disease), and controls to wear 5 accelerometer-based sensors on their chest and limbs for standardized in-clinic assessments and for 2 days at home. The study's aims were to assess the feasibility of use of wearable sensors, to determine the activity (lying, sitting, standing, walking) of participants, and to survey participants on their experience. RESULTS: Fifty-six individuals (16 with Parkinson disease, 15 with Huntington disease, 5 with prodromal Huntington disease, and 20 controls) were enrolled in the study. Data were successfully obtained from 99.3% (278/280) of sensors dispatched. On average, individuals with Huntington disease spent over 50% of the total time lying down, substantially more than individuals with prodromal Huntington disease (33%, p = 0.003), Parkinson disease (38%, p = 0.01), and controls (34%; p < 0.001). Most (86%) participants were "willing" or "very willing" to wear the sensors again. CONCLUSIONS: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received. These sensors can identify statistically significant differences in activity profiles between individuals with movement disorders and those without. In addition, continuous, objective monitoring can reveal disease characteristics not observed in clinic.