NDLI: Object-Centred Recognition of Human Activity

Content Provider	IEEE Xplore Digital Library
Author	Nabiei, R. Parekh, M. Jean-Baptiste, E. Jancovic, P. Russell, M.
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. & Syst. Eng., Univ. of Birmingham, Birmingham, UK (Nabiei, R.; Parekh, M.; Jean-Baptiste, E.; Jancovic, P.; Russell, M.)
Abstract	This paper describes an approach to real-time human activity recognition using hidden Markov models (HMMs) and sensorised objects, and its application to rehabilitation of stroke patients with apraxia or action disorganisation syndrome (AADS). Results are presented for the task of making a cup of tea. Unlike speech or other sequential decoding problems where HMMs have previously been successfully applied, human actions can occur simultaneously or at least in overlapping time. The solution proposed in this paper is based on a parallel, asynchronous set of detectors, each responsible for the detection of one of the component sub-goals of the tea-making task. The inputs to these detectors are formed from the outputs of sensors attached to the objects involved in that sub-goal, plus hand coordinate data. The sensors, comprising an accelerometer and three force-sensitive resistors, are packaged in a coaster which can be easily attached to the base of a mug or jug. In tests on complete tea-making trials, error rates range from less than 5% for sub-goals where all of the objects involved are sensorised, to up to 30% for detectors that rely on hand-coordinate data alone. The complete set of detectors runs in real-time. It is concluded that a set of parallel HMM-based sub-goal detectors combined with fully sensorised objects, is a viable, accurate and easily deployable approach to real-time object-centred human activity recognition.
Starting Page	63
Ending Page	68
File Size	491154
Page Count	6
File Format	PDF
e-ISBN	9781467395489
DOI	10.1109/ICHI.2015.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accelerometers Instruments Hidden Markov models Detectors Feature extraction Decoding
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models

Activity recognition from sensors using dyadic wavelets and Hidden Markov Model

Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer

Human activity recognition using LZW-Coded Probabilistic Finite State Automata

MobiRAR: Real-Time Human Activity Recognition Using Mobile Devices

Indirect activity recognition using a target-mounted camera

A compact formulation of turbo audio-visual speech recognition

Gait recognition based on anatomical knowledge

A handwritten character recognition system based on acceleration

Object-Centred Recognition of Human Activity

Similar Documents

Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models

Activity recognition from sensors using dyadic wavelets and Hidden Markov Model

Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer

Human activity recognition using LZW-Coded Probabilistic Finite State Automata

MobiRAR: Real-Time Human Activity Recognition Using Mobile Devices

Indirect activity recognition using a target-mounted camera

A compact formulation of turbo audio-visual speech recognition

Gait recognition based on anatomical knowledge

A handwritten character recognition system based on acceleration

Object-Centred Recognition of Human Activity