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Edge-based personal computing services: fall detection as a pilot study

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Current developments in information and electronic technologies have pushed a tremendous amount of applications to meet the demands of personal computing services. Various kinds of smart devices have been launched and applied in our daily lives to provide services for individuals; however, the existing computing frameworks including local silo-based and cloud-based architectures, are not quite fit for personal computing services. Meanwhile, personal computing applications exhibit special features, they are latency-sensitive, energy efficient, highly reliable, mobile, etc, which further indicates that a new computing architecture is urgently needed to support such services. Thanks to the emerging edge computing paradigm, we were inspired to apply the distributed cooperative computing idea at the data source, which perfectly solves issues occurring among existing computing paradigms while meeting the requirements of personal computing services. Therefore, we explore personal computing services utilizing the edge computing paradigm, discuss the overall edge-based system architecture for personal computing services, and design the conceptual framework for an edge-based personal computing system. We analyze the functionalities in detail. To validate the feasibility of the proposed architecture, a fall detection application is simulated in our preliminary evaluation as an example service in which three Support Vector Machine based fall detection algorithms with different kernel functions are implemented. Experimental results show edge computing architecture can improve the performance of the system in terms of total latency, with about 22.75% reduction on average in the case of applying 4G at the second hop even when the data and computing stream of the application is small.

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The authors would like to thank for Qiangyang Zhang for his help in experiments. The author also would like to thanks the anonymous reviewers for their valuable comments and suggestions. This research was supported by Natural Science Foundation of Shandong Province(ZR2018BF014), Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2017RCJJ042), and Key Laboratory for wisdom mine information technology of Shandong Province, Shandong University of Science and Technology.

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Correspondence to Lingmei Ren.

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Ren, L., Zhang, Q., Shi, W. et al. Edge-based personal computing services: fall detection as a pilot study. Computing 101, 1199–1223 (2019).

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