Smart Devices are Different

Stisen, Allan; Blunck, Henrik; Bhattacharya, Sourav; Prentow, Thor Siiger; Kjærgaard, Mikkel Baun; Dey, Anind K.; Sonne, Tobias; Jensen, Mads

doi:10.1145/2809695.2809718

Cited by 522 publications

(78 citation statements)

References 40 publications

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“…For example, the classifiers used to process the data (see “Developing behavioral measures from smartphone data” section) could introduce noise to the behavioral measures if they were developed for data collected from devices running one OS (e.g., Android), but are then used for data collected from devices running another OS (e.g., iOS). Such standardization issues even arise when a standard OS is used across devices made by different manufacturers or devices containing different makes of sensors (e.g., participants using different Android devices; Stisen et al, 2015). For this reason, in some smartphone-sensing studies, participants have been provided with mobile devices to use for the duration of the study so that all participants use the same device and OS (e.g., Wang et al, 2014); however, this approach may require participants to carry an extra phone with them and does not scale to studies with very large numbers of participants.…”

Section: Practical Considerations For Making Key Design Decisionsmentioning

confidence: 99%

Using Smartphones to Collect Behavioral Data in Psychological Science

Harari

Lane

Wang

et al. 2016

Perspect Psychol Sci

446

335

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Smartphones now offer the promise of collecting behavioral data unobtrusively, in situ, as it unfolds in the course of daily life. Data can be collected from the onboard sensors and other phone logs embedded in today’s off-the-shelf smartphone devices. These data permit fine-grained, continuous collection of people’s social interactions (e.g., speaking rates in conversation, size of social groups, calls, and text messages), daily activities (e.g., physical activity and sleep), and mobility patterns (e.g., frequency and duration of time spent at various locations). In this article, we have drawn on the lessons from the first wave of smartphone-sensing research to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain. It is our hope that these practical guidelines will facilitate the use of smartphones as a behavioral observation tool in psychological science.

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Section: Practical Considerations For Making Key Design Decisionsmentioning

confidence: 99%

Using Smartphones to Collect Behavioral Data in Psychological Science

Harari

Lane

Wang

et al. 2016

Perspect Psychol Sci

446

335

View full text Add to dashboard Cite

show abstract

“…This occurs due to the heterogeneities among the devices. The heterogeneities among various mobile devices, smart watches have been discussed in detail by Stisen et al (). The authors have investigated a large number of devices to study device heterogeneities and have proposed techniques to mitigate them.…”

Section: Future Directionsmentioning

confidence: 99%

Recent trends in machine learning for human activity recognition—A survey

Ramamurthy

Roy

2018

WIREs Data Min & Knowl

225

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There has been an upsurge recently in investigating machine learning techniques for activity recognition (AR) problems as they have been very effective in extracting and learning knowledge from the activity datasets. The technique ranges from heuristically derived hand-crafted feature-based traditional machine learning algorithms to the recently developed hierarchically self-evolving feature-based deep learning algorithms. AR continues to remain a challenging problem in uncontrolled smart environments despite the amount of work contributed by the researcher in this field. The complex, volatile, and chaotic nature of the activity data presents numerous challenges that influence the performance of the AR systems in the wild. In this article, we present a comprehensive overview of recent machine learning and data mining techniques generally employed for AR and the underpinning problems and challenges associated with the existing systems. We also articulate the recent advances and state-of-the-art techniques in this domain in an attempt to identify the possible directions for future AR research.

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“…It focuses on recognizing physical activities (Baños et al 2012). & The HHAR (Heterogeneity HAR) study analysed various heterogeneities in motion sensor-based sensing (i.e., sensor biases, sampling rate heterogeneity and sampling rate instability) and their impact on HAR by sensing a set of activities with 13 different smartphones (Stisen et al 2015). & The AReM dataset measures the Received Signal Strength (RSS) between bodyworn sensors in an experiment focused on recognizing physical activities (Palumbo et al 2016).…”

Section: Review Of Public Datasetsmentioning

confidence: 99%

Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

et al. 2020

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Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.

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Smart Devices are Different

Cited by 522 publications

References 40 publications

Using Smartphones to Collect Behavioral Data in Psychological Science

Using Smartphones to Collect Behavioral Data in Psychological Science

Recent trends in machine learning for human activity recognition—A survey

Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

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