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      Use of Low-Resolution Infrared Pixel Array for Passive Human Motion Movement and Recognition

      Authors: , , ,

      Publication date (Print):

      Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Conference theme: Human Computer Interaction Conference

      Conference date: 4 - 6 July 2018

      Keywords: Infrared sensors, Human activity detection, Classification methods, Elderly care homes

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          Abstract

          The daily monitoring of ageing population is a current issue which can be effectively tackled by applying daily activity monitoring via smart sensing technology. The purpose of the monitoring is mostly aimed at collecting health conditional related activity awareness and emergency events detection. This is a pilot study that uses low pixel resolution infrared sensors for nonintrusive human activity detection and recognition without body attachments and taking of individual image. In this work, we design and implement a multiple IR sensors system and a serial experiment to verify the availability of applying low-resolution IR data for human activity recognition for both single and multiple target scenarios in the healthcare context. In the experimental setup, the sensor system achieves 82.44% accuracy in general and reaches 100% accuracy rate for some particular activities. The work proves that the low-resolution IR information is an effective metric for human activity monitoring in healthcare applications.

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          Stochastic Gradient Descent Tricks

           Léon Bottou (2012)
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            Support Vector Machines for Classification and Regression

             S. GUNN,  SR Gunn,  S.R. Gunn … (1998)
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              Human daily activity recognition with sparse representation using wearable sensors.

               Mi Zhang,  Alexander A. Sawchuk (2013)
              Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l(1) minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.
              Article 0 comments Cited 20 times – based on 0 reviews     Review now
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                Author and article information

                Affiliations
                Coventry University

                Coventry, United Kingdom
                Contributors
                Yordanka Karayaneva
                Samuel Baker
                Bo Tan
                Yanguo Jing
                Conference
                Publication date: July 2018
                Publication date (Print): July 2018
                Pages: 1-2
                ID: 10.14236/ewic/HCI2018.143
                Copyright statement: © Karayaneva et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                License:

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference
                Conference acronym: HCI
                Conference number: 32
                Conference location: Belfast, UK
                Conference date: 4 - 6 July 2018
                Conference sponsor: Electronic Workshops in Computing (eWiC)
                Conference theme: Human Computer Interaction Conference
                Product
                Self URI (journal page): https://ewic.bcs.org/

                Link to data:

                ScienceOpen disciplines: Applied computer science, Computer science, Security & Cryptology, Graphics & Multimedia design, General computer science, Human-computer-interaction

                Keywords: Elderly care homes, Infrared sensors, Human activity detection, Classification methods

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