Mobile Technology for Adaptive Aging Proceedings of a Workshop (2020) / Chapter Skim
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6 Sensors in Support of Aging-in-Place: The Good, the Bad, andthe Opportunities - Diane Cook
6 Sensors in Support of Aging-in-Place: The Good, the Bad, andthe Opportunities - Diane Cook
Pages 105-126
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In this chapter, we examine the state of the science in sensor technologies and their ability to promote successful aging. We review recent developments in sensor design and behavior marker discovery as well as their roles in automating health assessment and intervention.
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. Technology holds a promise to meet some of the coming age wave needs by automating and dramatically scaling health assessment and treatment.
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Table 6-1 lists sensors that are commonly used for ubiquitous healthcare because they provide moment-by-moment human behavior markers, in situ. Here, we discuss the potential use cases for sensor data as well as the pros and cons for alternative sensor types.
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As a result, sophisticated software is required to understand behavior patterns and health states from these data. In contrast with ambient sensors, wearable sensors both require much more user attention and provide a much larger data set.
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TABLE 6-2 Behavioral Markers that Are Extracted from Sensor Data Category Features Mobility step count, walking speed, step length, daily distance covered, number and duration of times in one spot, number walking bouts, activity level Exercise number, duration, movement types, intensity, location Sleep number and duration of daily sleep bouts, sleep times, sleep locations, sleep fitfulness, sleep interruptions, sleep apnea Activity number, duration, and location of basic and instrumental activities of daily living Environment frequented locations with type, outdoor walkability score, indoor and outdoor air quality, temperature, light levels, sound levels, number of residents, environment clutter Devices types of device interactions, medication frequency, use of compensatory devices Socialization number and duration of incoming/outgoing phone calls, text messages, missed calls, address book, calendar, time out of home, number and duration of visitors, activity before and after calls Circadian and complexity of daily routine, number of daily activities, minimum and maximum diurnal rhythm inactivity times, daily variance in activity and mobility parameters, periodogram derived circadian rhythm
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Wearable sensors have traditionally been employed to recognize movementbased activities (e.g., sit, stand, walk, climb, lie down) , while ambient sensors typically label basic and instrumental activities of daily living (e.g., work, exercise, relax, cook, eat, entertain, sleep)
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As Figure 6-2 illustrates, automated assessment relies on large sensor data and corresponding behavior markers. Here, we review recent studies and findings that automate assessment of factors contributing to ging in place, including motor functioning, cognition, mood, and funca tional independence.
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. Such motor function can be assessed by ambient sensors in addition to wearable sensors.
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Behavior parameters over time were found to correlate with diverse health parameters, including fall risk, functional performance, cognitive function, motor function, and dyskinesia "on" states.
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, then issued a prompt if the activity was not initiated at the predicted time. Not only can sensor data inform intervention design, but they can also provide a valuable means to understand treatment adherence.
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BARRIERS AND OPPORTUNITIES There has been a flurry of activity in the space of pervasive computing and machine learning–driven analysis of human behavior data. These advances set the stage for tremendous technological support of aging in place.
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Using these procedures, sensors in a smart home can "train" a smartwatch on how to recognize classes of behaviors. Once the individual leaves home, the smartwatch can continue observing behavior where the home left off and can update the home's models when it returns.
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Chances are you have no idea whether real-world risks exist. Here is how I matched patient names to publicly available health data sold by Washington State, and how the state responded. Doing this kind of experiment helps improve data-sharing practices, reduce privacy risks, and encourage the devel pment of better technological solutions.
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As a result, expensive smartwatches or smart homes will not be a high-priority expenditure. Unless external agencies support sensor technology costs or prices
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Ubiquitous ambient and mobile sensors collect large amounts of continuous data. By processing these data, machine learning techniques extract behavioral markers and map behavior features to clinical assessment scores, providing automated assessment of physical, mental, and emotional health.
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J Ortiz et al., "Toward ultra-low-power remote health monitoring: An optimal and adaptive compressed sensing framework for activity recognition," IEEE Trans.
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K Das, "HuMAn: Complex activity recognition with multi-modal multi-positional body sensing," IEEE Trans.
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Cook, and M Schmitter-Edgecombe, "Automated cognitive health assessment using smart home monitoring of complex tasks," IEEE Trans.
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Cook, "Indirectly-supervised anomaly detection of clinically- meaningful health events from smart home data," ACM Trans. Intell.
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Skodras, "On the comparison of wearable sensor data fusion to a single sensor machine learning technique in fall detection," Sensors, vol.
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Kientz, and L.R. Pina, "Making sense of sleep sensors: How sleep sensing technologies support and undermine sleep health," in Confer ence on Human Factors in Computing Systems, 2017.
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