Toward Mobile Sensor Fusion Platform for Context-Aware Services

Sashima, Akio; Ikeda, Takeshi; Kurumatani, Koichi

doi:10.5772/7030

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…[ 115 ] and Sashima et al . [ 116 ] created client-server platforms that monitor the activities in a space with a constant connection with a server. This causes major energy consumption and decreases the capabilities of the mobile devices in the detection of the activities performed.…”

Section: Sensor Data Fusion In Mobile Devicesmentioning

confidence: 99%

“…The recognition of the ADLs [ 81 , 110 , 131 , 141 , 142 ] is very important in systems developed to support and monitor elderly people or people with some disease or impairment [ 143 , 144 ]. When combined with the user’s agenda, they can be employed for monitoring and learning lifestyles and physical exercise, helping people in emergency situations [ 116 , 136 ] and other situations where the sensors can improve life quality, e.g., driving monitoring [ 114 ].…”

Section: Application In Ambient Assisted Livingmentioning

confidence: 99%

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From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Pires

García

Flórez-Revuelta

2016

Sensors

138

105

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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Section: Sensor Data Fusion In Mobile Devicesmentioning

confidence: 99%