Modeling patterns of activities using activity curves

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter‐Edgecombe, Maureen

doi:10.1016/j.pmcj.2015.09.007

Cited by 48 publications

(25 citation statements)

References 43 publications

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“…In the case of work by Dodge et al [80], by Hodges et al [81], by Dawadi et al [82], and by Lotfi et al [83], unexpected behavioral patterns are viewed as a health risk for individuals who are at risk of cognitive decline. These researchers have found that an increase in the number of activity anomalies and variation in behavior patterns such as activity times and walking speed are correlated with changes in cognitive health.…”

Section: Acting In Response To Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

Self Cite

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Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

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Section: Acting In Response To Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

Self Cite

View full text Add to dashboard Cite

show abstract

“…In regards to change detection approaches, a handful of studies have investigated change that can be detected in human behavior patterns. These approaches have quantified change statistically [ 12 , 13 , 14 ], graphically [ 13 , 15 , 16 ], and algorithmically [ 15 , 17 , 18 , 19 ]. Recently, Merilahti et al [ 12 ] extracted actigraphy-based features from data collected for at least one year.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation

Sprint

Cook

Weeks³

et al. 2017

Sensors

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Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers.

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“…The authors showed that combining mutual information based weighting of sensor events and adding past contextual information to the feature leads to better performance. To analyse possible changes in cognitive or physical health, Dawadi et al [42] introduced the notion of activity curve and proposed a permutation-based change detection in activity routine algorithm. The authors validated their approach with a two-year smart home sensor data.…”

Section: Related Workmentioning

confidence: 99%

Using Sensors to Study Home Activities

Jiang

Pozza

Gunnarsdóttir

et al. 2017

JSAN

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Abstract:Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

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Modeling patterns of activities using activity curves

Cited by 48 publications

References 43 publications

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation

Using Sensors to Study Home Activities

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