NDLI: Recognizing gym exercises using acceleration data from wearable sensors

Content Provider	IEEE Xplore Digital Library
Author	Koskimaki, H. Siirtola, P.
Copyright Year	2014
Description	Author affiliation: Comput. Sci. & Eng. Dept., Univ. of Oulu, Oulu, Finland (Koskimaki, H.; Siirtola, P.)
Abstract	The activity recognition approaches can be used for entertainment, to give people information about their own behavior, and to monitor and supervise people through their actions. Thus, it is a natural consequence of that fact that the amount of wearable sensors based studies has increased as well, and new applications of activity recognition are being invented in the process. In this study, gym data, including 36 different exercise classes, is used aiming in the future to create automatic activity diaries showing reliably to end users how many sets of given exercise have been performed. The actual recognition is divided into two different steps. In the first step, activity recognition of certain time intervals is performed and in the second step the state-machine approach is used to decide when actual events (sets in gym data) were performed. The results showed that when recognizing different exercise sets from the same occasion (sequential exercise sets), on average, over 96 percent window-wise true positive rate can be achieved, and moreover, all the exercise events can be discovered using the state-machine approach. When using a separate validation test set, the accuracies decreased significantly for some classes, but even in this case, all the different sets were discovered for 26 different classes.
Starting Page	321
Ending Page	328
File Size	694190
Page Count	8
File Format	PDF
ISBN	9781479945184
DOI	10.1109/CIDM.2014.7008685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	event recognition Presses Accuracy gym data Radiation detectors Shoulder accelerometer Activity recognition wearable sensors Wearable sensors Testing
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

A comparison of classifiers for activity recognition using multiple accelerometer-based sensors

What Can an Arm Holster Worn Smart Phone Do for Activity Recognition?

Recognizing short duration hand movements from accelerometer data

Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Recognizing user activity based on accelerometer data from a mobile phone

Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix

A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer

A framework for human activity recognition based on accelerometer data

Recognizing gym exercises using acceleration data from wearable sensors

Similar Documents

A comparison of classifiers for activity recognition using multiple accelerometer-based sensors

What Can an Arm Holster Worn Smart Phone Do for Activity Recognition?

Recognizing short duration hand movements from accelerometer data

Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Recognizing user activity based on accelerometer data from a mobile phone

Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix

A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer

A framework for human activity recognition based on accelerometer data

Recognizing gym exercises using acceleration data from wearable sensors