Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Cook, Diane J.; Schmitter‐Edgecombe, Maureen

doi:10.1109/access.2021.3076362

Cited by 13 publications

(5 citation statements)

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“…This approach demonstrated a recognition f1 score of 0.85 for 12 activities from 250 individuals in prior work: chores, eat, entertain, errands, exercise, hobby, hygiene, relax, school, sleep, travel, and work. 70 We use the pretrained model for the remainder of the experiments described in this paper.…”

Section: Collecting and Labeling Activity Datamentioning

confidence: 99%

“…We compute and compile the digital behavior markers that become a person's behavior profile. 70,71 The markers are defined in ►Table 1 and are gathered for each sensor (existing and new) and activity class at multiple time resolutions (e.g., hourly, daily). Our software to generate these markers is available online.…”

Section: Defining and Extracting Digital Behavior Markersmentioning

confidence: 99%

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Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

2022

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Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures. Methods We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures. Results We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

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Section: Collecting and Labeling Activity Datamentioning

confidence: 99%

Section: Defining and Extracting Digital Behavior Markersmentioning

confidence: 99%

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

2022

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“…The relationship between activities and cognitive/functional impairment using a data-driven approach is a less-investigated topic due to the limitations of collecting real-world datasets. Researchers have managed to collect multi-modal datasets [33] to predict the clinical health scores by fusing ambient and mobile sensor features into a behaviorome. However, this scheme involves multiple stages of training a module-specific shallow learning model to combine the multi-modal data and predict the clinical health score.…”

Section: Related Workmentioning

confidence: 99%

CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry

Ramamurthy

Chatterjee

Galik

et al. 2022

2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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An individual's cognitive and functional abilities are commonly assessed through physical and mental status examination, observational performance measures, surveys and proxy reports of symptoms. These strategies are not ideal for early impairment detection as the individual needs to be present physically at the clinic to avail the assessments, especially for older adults who require assistance from a caregiver, and experience mobility, cognitive and functional disabilities from neurodegenerative disorders. Moreover, these strategies rely on self-reporting and proxy reports for evaluation which often leads to under-reporting of symptoms and decrease the validity of these measures. We argue that an early assessment of functional, and cognitive health impairment can be obtained from the individual's daily activities captured through accelerometry. In this work, we postulate to learn high-level motion related representations from accelerometer data to better correlate with underlying functional and cognitive health parameters of older adults using a contrastive and multi-task learning framework. In particular, we posit a novel indicator, Impairment Indicator using the proposed multi-task learning framework that can indicate functional or cognitive decline as neurodegenerative disease progresses. An extensive 24-hour data collection from 25 older adults with the clinician in-the-loop was carried out in a retirement community center with IRB approval. We collected the activity patterns using wearables in their homes in addition to survey-based assessments and observational performance measures recorded by a clinical evaluator to infer their current cognitive and functional impairment status. Our evaluation on the acquired dataset reveals that the representations learned using contrastive learning aids in improving the detection of activities, activity performance score, and stage of dementia to 92%, 97%, and 98%, respectively.

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“…Not too surprisingly, most successful real-world, long-term research using sensor technology with older adults has focused on contactless, zero-interaction approaches 9,13,28,[35][36][37][38][39][40] . Such technologies include passive infrared (PIR) motion sensors that capture an individual's activity in a given room 9,28,35,36,38,41,42 , contact door sensors that can signal when a person leaves or enters the home [36][37][38]43 , pressure sensors on or under a mattress that capture sleep measures 9,36,39 , and electronic pillboxes to track medication adherence 44 , along with more obtrusive depth-sensing cameras that track silhouettes to detect falls and monitor gait parameters 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Two notable real-world examples of using extensive sets of digital measures are studies by Cook et al 42 and Chen et al 20 , which demonstrate the feasibility of using a digital exhaust based on wearable and contactless sensors to predict multiple clinical scores (in the former) and MCI (in the latter). Building on their work, we aim to evaluate the potential of a systems oriented approach towards long-term remote health-monitoring in the demographics of older adults.…”

Section: Introductionmentioning

confidence: 99%

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

et al. 2022

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Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

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Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Cited by 13 publications

References 46 publications

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

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