Micro-context recognition of sedentary behaviour using smartphone

Fahim, Muhammad; Khattak, Asad Masood; Baker, Thar; Chow, Francis; Shah, Babar

doi:10.1109/dictap.2016.7543996

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Smartphones have also been employed for crowdsourcing and context recognition (such as indoor vs. outdoor, moving vs. stationary, etc.) [35,[68][69][70][71]. However, smartphone-based AR systems are not sufficiently accomplished to detect or recognize activities involving hand gestures and arm movements.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, natural user behavior gets disregarded, which poorly affects the performance of such AR models in-the-wild. A few research studies [34][35][36][37][38][39] have emphasized utilizing either smartphone sensors or heterogeneous sensors for identifying human contexts, e.g., "indoor" vs. "outdoor", "in a car" vs. "in a bus", "sitting" vs. "driving", etc. However, these schemes fail to model human-environment interactions in-the-wild to infer the natural users' context in combination with their primary physical activity.…”

Section: Introductionmentioning

confidence: 99%

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Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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“…In order to mine the contexts of sedentary behaviour, there is a need to develop a ubiquitous system that can track the sedentary elapsed time accurately with all its minor routines ranging from office work to watching television during leisure time. Previously, we conducted a pilot study on micro-context recognition [ 14 ] and visualizing the user behaviour over the web through the Internet. In this paper, we propose a user-centric smartphone-based approach to recognize the context of sedentary behaviour based on the onboard accelerometers and audio sensors of the smartphone.…”

Section: Introductionmentioning

confidence: 99%

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

Fahim

Baker

Khattak

et al. 2018

Sensors

Self Cite

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Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active.

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“…However, the application of these systems is limited to a narrow field of livestock sheds, so it is difficult to be applied in the free-range grazing system. Muhammad Fahim et al [16][17][18] proposed a method of using the accelerometer sensor of the smart phone to recognize user situations (i.e., still or active) and developed a cloud-based smart phone application supported by a web-based interface to visualize the sedentary behavior. Complex and large-scale sensor communication [19] is needed under these circumstances.…”

Section: Introductionmentioning

confidence: 99%

The Platform Development of a Real-Time Momentum Data Collection System for Livestock in Wide Grazing Land

Zhang

Kim

Lee³

2018

Electronics

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In the process of animal husbandry production through grazing, animals are active in large grassland or mountain areas, and it is very difficult to obtain and deal with the information on animal activity and state of life. In this paper, we propose a platform for operation of data transmission and analysis system which gathers activity and status information of livestock. The data collected in real time from integrated livestock sensor modules are anticipated to assist farmers to supervise animal activities and health. While at the same time the improvements of viable farming techniques are expected to reduce the impact of the livestock industry on the environment. For the individual management of target livestock, the data collection system needs a convergence technology with a physical active sensor, a wireless sensor network and solar power technology to cover the wide area of mountains. We implemented a momentum data collection system to collect and transfer the information of ecological and situations of livestock in grazing, which include sensor and communication modules, repeaters with solar panels to cover the problems of communications in wide grazing and a receiver connected to main server. Besides, in order to prevent data collisions and deviations on multiple transmitter operation, we renewed the format of the communication protocol and made a platform to analyze animal activities information by software. Finally, the system and platform were applied and tested in National Rural Development Administration in Republic of Korea.

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Micro-context recognition of sedentary behaviour using smartphone

Cited by 5 publications

References 14 publications

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

The Platform Development of a Real-Time Momentum Data Collection System for Livestock in Wide Grazing Land

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