Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future

Physical Activity Recognition and Identification System

Background: It is well-established that physical activity is beneficial to health. It is less known how the characteristics of physical activity impact health independently of total amount. This is due to the inability to measure these characteristics in an objective way that can be applied to large population groups. Accelerometry allows for objective monitoring of physical activity but is currently unable to identify type of physical activity accurately. Methods: This thesis details the creation of an activity classifier that can identify type from accelerometer data. The current research in activity classification was reviewed and methodological challenges were identified. The main challenge was the inability of classifiers to generalize to unseen data. Creating methods to mitigate this lack of generalisation represents the bulk of this thesis. Using the review, a classification pipeline was synthesised, representing the sequence of steps that all activity classifiers use. 1. Determination of device location and setting (Chapter 4) 2. Pre-processing (Chapter 5) 3. Segmenting into windows (Chapters 6) 4. Extracting features (Chapters 7,8) 5. Creating the classifier (Chapter 9) 6. Post-processing (Chapter 5) For each of these steps, methods were created and tested that allowed for a high level of generalisability without sacrificing overall performance. Results: The work in this thesis results in an activity classifier that had a good ability to generalize to unseen data. The classifier achieved an F1-score of 0.916 and 0.826 on data similar to its training data, which is statistically equivalent to the performance of current state of the art models (0.898, 0.765). On data dissimilar to its training data, the classifier achieved a significantly higher performance than current state of the art methods (0.759, 0.897 versus 0.352, 0.415). This shows that the classifier created in this work has a significantly greater ability to generalise to unseen data than current methods. Conclusion: This thesis details the creation of an activity classifier that allows for an improved ability to generalize to unseen data, thus allowing for identification of type from acceleration data. This should allow for more detailed investigation into the specific health effects of type in large population studies utilising accelerometers

A COMPREHENSIVE VALIDATION OF ACTIVITY TRACKERS FOR ESTIMATING PHYSICAL ACTIVITY AND SEDENTARY BEHAVIOR IN FREE-LIVING SETTINGS

The aim of study one of this dissertation was to compare consumer activity trackers (ATs) with the research-grade ActiGraph™ GT3X-BT accelerometer (AG) in estimating energy expenditure (EE) and steps during orbital shaking at different frequencies. To address this aim, we utilized an electronic orbital shaking protocol (twenty-four, 3-minute trials; 2-hour trials). For all comparisons, the AG served as the reference measure. In the 3-min protocol, we showed that on average, the NL-1000 pedometer (NL) produced the lowest error (-9 steps/3-min) at 0.9 Hz (corresponding to moderate intensity). The magnitude of the error for the NL was 14 steps/3-min at a 3.0 Hz frequency (corresponding to very vigorous intensity). For the 2-hr protocol, estimates from all others were equivocal, with some overestimating steps (bias range: 1,331 steps/2-hrs for the Misfit Shine to 1,921 steps/2-hrs for the Misfit Flash [MFF]). For estimated EE bias ranged from26.6 kcals/2-hrs for the MFF to 45.8 kcals/2-hrs for the Misfit Shine. For other ATs, steps were underestimated (bias range: -5,770 steps/2-hrs for the Garmin Vivofit [GV] to -570 steps/2-hrs for the NL). For EE, the bias ranged from -436.8 kcals/2-hrs for the GV to -261.7 kcals/2-hrs for the Fitbit Flex [FBF]). This study provides evidence about the differences in prediction algorithms by device across a broad range of oscillation frequencies that corresponded to different PA intensity levels. For study two, we sought determine the accuracy and precision of activity trackers (ATs) in estimating steps, EE, activity minutes and sedentary time compared to direct observation (DO)-derived measures (criterion measures) in free-living settings. We also validated commonly used research-grade devices (e.g. hip-worn AG (AGhip), wrist-worn AG (AGwrist). Thirty-two healthy men and women (50% female, 37.5% minority; mean ± SD: Age = 32.3 ± 13.3 years; BMI = 24.4 ± 3.3 kg∙m-2) were directly observed while completing three, 2-hour visits on different days while wearing ten ATs, three research-grade devices and a biometric shirt. A validated DO system was used to derive criterion measures for activity and sedentary time (ST) outcomes. ATs were accurate with varying precision in estimating physical activity (PA) behaviors in free-living settings. Additionally, ATs and research-grade accelerometers performed similarly (e.g. more accurate in estimating steps and less accurate in estimating moderate-to-vigorous PA [MVPA] minutes). For all devices, step estimates were accurate and strongly correlated (r range: 0.91 for the Apple iWatch to 0.97 for the AGhip) with criterion measures but EE and MVPA estimates were less accurate and more variable (EE: r = 0.32 [GV] to r = 0.85 [AGhip]; MVPA: r = 0.2 [NL] to r = 0.75 [AGhip]). For ATs, estimates of sedentary time were the least accurate and weakly correlated (r=0.06 Fitbit One [FBO] and FBF) with criterion measures derived from DO. Implications from this study are that consumers and the research community using ATs such as Fitbit (FB) to track steps can be confident in estimating steps but less confident in estimating sedentary time. This study advances our understanding of the performance characteristics of ATs in free-living natural settings using a validated DO method to derive PA and ST measures. This work significantly advances the field of activity monitor validation that should set the standard for future work. The aims of study three were: 1) to examine the ability of ATs to detect change in PA and ST in free-living settings and 2) to examine the ability of research-grade accelerometers to detect change in PA and ST in free-living settings. To address these aims, we used an innovative approach to analyze data from study two. We defined change as a visit-to-visit difference that was greater than the within-subject standard deviation of the criterion measure (estimated by a linear-mixed model). Confusion matrices were used to examine percent agreement between DO visit-to-visit change and device visit-to-visit change. Key findings were focused on the widely used FBO and FBF and research-grade devices. We showed that, there was similar agreement between the hip-worn FBO and FBF with AGhip and AGwrist in estimates of change in steps (89.1% FBO, 88.8% FBF and 88.3% AGwrist, 91.4% AGhip correct classification), EE (73.4% FBO, 70.6% FBF and 77.0% AGhip correct classification) and MVPA minutes (accept FBF) (79.7% FBO, 65.2% FBF and 71.2% AGwrist, 77.0% AGhip correct classification) with criterion measured change. However, change in ST was more difficult to detect for the FB and AGhip (46.8% FBO, 42.3% FBF, 53.1% AGhip and 72.7% AGwrist correct classification). This novel study provides evidence that as an alternative to research-grade accelerometers, researchers may employ FB to measure step accumulation pre- and post-intervention and have a satisfactory level of confidence in FB change detection. This work significantly advances the field of activity monitor validation research and informs intervention practices that should set the standard for future work. This body of work provides the first comprehensive validation of ATs from highly controlled orbital shaker testing to directly-observed free-living settings. This translational research which has broad applications for using ATs for surveillance and intervention research and by the consumer

The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland-Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes

Detecting Periods of Eating in Everyday Life by Tracking Wrist Motion — What is a Meal?

Eating is one of the most basic activities observed in sentient animals, a behavior so natural that humans often eating without giving the activity a second thought. Unfortunately, this often leads to consuming more calories than expended, which can cause weight gain - a leading cause of diseases and death. This proposal describes research in methods to automatically detect periods of eating by tracking wrist motion so that calorie consumption can be tracked. We first briefly discuss how obesity is caused due to an imbalance in calorie intake and expenditure. Calorie consumption and expenditure can be tracked manually using tools like paper diaries, however it is well known that human bias can affect the accuracy of such tracking. Researchers in the upcoming field of automated dietary monitoring (ADM) are attempting to track diet using electronic methods in an effort to mitigate this bias. We attempt to replicate a previous algorithm that detects eating by tracking wrist motion electronically. The previous algorithm was evaluated on data collected from 43 subjects using an iPhone as the sensor. Periods of time are segmented first, and then classified using a naive Bayesian classifier. For replication, we describe the collection of the Clemson all-day data set (CAD), a free-living eating activity dataset containing 4,680 hours of wrist motion collected from 351 participants - the largest of its kind known to us. We learn that while different sensors are available to log wrist acceleration data, no unified convention exists, and this data must thus be transformed between conventions. We learn that the performance of the eating detection algorithm is affected due to changes in the sensors used to track wrist motion, increased variability in behavior due to a larger participant pool, and the ratio of eating to non-eating in the dataset. We learn that commercially available acceleration sensors contain noise in their reported readings which affects wrist tracking specifically due to the low magnitude of wrist acceleration. Commercial accelerometers can have noise up to 0.06g which is acceptable in applications like automobile crash testing or pedestrian indoor navigation, but not in ones using wrist motion. We quantify linear acceleration noise in our free-living dataset. We explain sources of noise, a method to mitigate it, and also evaluate the effect of this noise on the eating detection algorithm. By visualizing periods of eating in the collected dataset we learn that that people often conduct secondary activities while eating, such as walking, watching television, working, and doing household chores. These secondary activities cause wrist motions that obfuscate wrist motions associated with eating, which increases the difficulty of detecting periods of eating (meals). Subjects reported conducting secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals, compared to only 6.8% of the time in a cafeteria dataset. Walking motion was found during 5.5% of the time during meals in free-living, compared to 0% in the cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved the average per person accuracy from 74% to 77% on our free-living dataset (t[353]=7.86, p\u3c0.001). This suggests that future data collections for eating activity detection should also collect detailed ground truth on secondary activities being conducted during eating. Finally, learning from this data collection, we describe a convolutional neural network (CNN) to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts appx 1-5 sec. The novelty of our new approach is that we analyze a much longer window (0.5-15 min) that can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We found that accuracy at detecting eating increased by 15% in longer windows compared to shorter windows. Overall results on CAD were 89% detection of meals with 1.7 false positives for every true positive (FP/TP), and a time weighted accuracy of 80%