A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

被引:1
|
作者
Maiti, Ananda [1 ]
Ye, Anjia [2 ]
Schmidt, Matthew [3 ]
Pedersen, Scott [2 ]
机构
[1] Univ Tasmania, Sch ICT, CoSE, Launceston, Tas 7248, Australia
[2] Univ Tasmania, Act Work Lab, CALE, Launceston, Tas 7248, Australia
[3] Univ Tasmania, Sch Hlth Sci, CoHM, Launceston, Tas 7248, Australia
关键词
privacy-preserving; privacy; time series; smart office; smart building; Internet of Things; microcontroller; eHealth; sedentary behavior; activity recognition; WORKPLACE INTERVENTION; OCCUPANCY DETECTION; SITTING PATTERNS; IMPACT;
D O I
10.3390/s23042229
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants' privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person's sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system's performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.
引用
收藏
页数:18
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