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In-home Activity and Micro-motion Logging Using Mobile Robot with Kinect

Published: 28 November 2016 Publication History

Abstract

In this paper, we propose a method for logging micro-motion of in-home daily activity based on the skeleton recognition of the elderly in their daily life. We believe that in near future, many types of mobile robots will be spread to general household, and our idea is to let such a home robot be equipped with a 3D-depth camera such as Microsoft Kinect to enable tracking and observation of the elderly people at any location, any time, from any angle at home. There are lots of furniture and other items at home, which often make hard to set fixed-point observation, but robots are flexible to move to the best position to acquire the motion logging. The collected micro-motion data can be used for early detection of mild cognitive impairment (MCI) or depression, both of which often affect the physical body ability. Our robot moves in the vicinity of the elderly and performs a joint detection from 3D depth information. Through the experiment in the real home, we could recognize the in-home activities and micro-motions with high accuracy.

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Cited By

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  • (2021)Activity Recognition for Assisting People with DementiaContactless Human Activity Analysis10.1007/978-3-030-68590-4_10(271-292)Online publication date: 24-Mar-2021
  • (2020)Home Activity Recognition Using Aggregated Electricity Consumption Data2020 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP50058.2020.00068(302-307)Online publication date: Sep-2020
  • (2018)Feasibility of human activity recognition using wearable depth camerasProceedings of the 2018 ACM International Symposium on Wearable Computers10.1145/3267242.3267276(92-95)Online publication date: 8-Oct-2018

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cover image ACM Other conferences
MOBIQUITOUS 2016: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services
November 2016
280 pages
ISBN:9781450347594
DOI:10.1145/3004010
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 28 November 2016

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Author Tags

  1. 3D depth camera
  2. activity recognition
  3. mobile sensor

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  • Research-article
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MOBIQUITOUS 2016
MOBIQUITOUS 2016: Computing Networking and Services
November 28 - December 1, 2016
Hiroshima, Japan

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

View all
  • (2021)Activity Recognition for Assisting People with DementiaContactless Human Activity Analysis10.1007/978-3-030-68590-4_10(271-292)Online publication date: 24-Mar-2021
  • (2020)Home Activity Recognition Using Aggregated Electricity Consumption Data2020 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP50058.2020.00068(302-307)Online publication date: Sep-2020
  • (2018)Feasibility of human activity recognition using wearable depth camerasProceedings of the 2018 ACM International Symposium on Wearable Computers10.1145/3267242.3267276(92-95)Online publication date: 8-Oct-2018

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