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Activity Recognition From User-Annotated Acceleration Data

L. Bao, S. Intille
Published 2004 · Computer Science

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In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.
This paper references
UbiComp 2002: Ubiquitous Computing
G. Borriello (2002)
Validity and reliability of the ExperienceSampling Method
M. Csikszentmihalyi (1987)
Measuring daily behavior using ambulatory accelerometry: The Activity Monitor
J. B. J. Bussmann (2001)
Validity and Reliability of the Experience-Sampling Method
M. Csikszentmihalyi (1987)
dy - namic identification of physiological and activity context in wearable computing
D. P. Siewiorek A. Krause (2003)
WearNET: A Distributed Multi-sensor System for Context Aware Wearables
P. Lukowicz (2002)
Recognizing human motion with multiple acceleration sensors
Jani Mäntyjärvi (2001)
Data mining: practical machine learning tools and techniques, 3rd Edition
I. Witten (1999)
Physical activity monitoring based on accelerometry: validation and comparison with video observation
K. Aminian (2006)
Unsupervised, dynamic identification of physiological and activity context in wearable computing
Andreas Krause (2003)
Context awareness by analysing accelerometer data
C. Randell (2000)
Estimation of speed and incline of walking using neural network
K. Aminian (1994)
A useful method for measuring daily physical activity by a three-direction monitor.
A. Sugimoto (1997)
Life patterns : structure from wearable sensors
B. Clarkson (2002)
Physical activity recognition from acceleration data under semi-naturalistic conditions
L. Bao (2003)
Multi-sensor Activity Context Detection for Wearable Computing
Nicky Kern (2003)
Data Mining
I. Witten (2000)
Acquiring in situ training data for context-aware ubiquitous computing applications
S. Intille (2004)
A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity
C. Bouten (1997)
Real-time motion classi ca-tion for wearable computing applications
R. W. DeVaul (2001)
The utility of the Digi-walker step counter to assess daily physical activity patterns.
G. Welk (2000)
Hoarder Board Specifications
V. Gerasimov (2002)
Multi-sensor context aware clothing
Kristof Van Laerhoven (2002)
What shall we teach our pants?
Kristof Van Laerhoven (2000)
Recognition of walking behaviors for pedestrian navigation
Seon-Woo Lee (2001)
李幼升 (1989)
Data mining: practical machine learning tools and techniques with Java implementations
I. Witten (1999)
Activity and Location Recognition Using Wearable Sensors
Seon-Woo Lee (2002)
Hierarchical recognition of intentional human gestures for sports video annotation
Graeme S. Chambers (2002)
The prediction of speed and incline in outdoor running in humans using accelerometry.
R. Herren (1999)
Ambulatory monitoring of physical activity in working situations, a validation study.
M. Uiterwaal (1998)
Hoarder Board Specifications, Access date
V Gerasimov (2002)
Detection of posture and motion by accelerometry : a validation study in ambulatory monitoring
F. Förster (1999)

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PerRNN: Personalized Recurrent Neural Networks for Acceleration-Based Human Activity Recognition
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Inertial Sensors for Kinematic Measurement and Activity Classification of Gait Post-Stroke
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Review of accelerometry for determining daily activity among elderly patients.
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Stress arousal monitoring in natural environments
Martin Kusserow (2012)
Feature selection based on mutual information for human activity recognition
Benjamin Fish (2012)
Dealing with human variability in motion based, wearable activity recognition
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Classifying Human Activities with Temporal Extension of Random Forest
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MEMS-based human activity recognition using smartphone
Ya Tian (2016)
Exploring unconstrained mobile sensor based human activity recognition
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A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer
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Gait monitoring for human activity recognition using perceptive shoe based on hetero-core fiber optics
Yuya Koyama (2016)
Manifold Learning and Recognition of Human Activity Using Body-Area Sensors
Mi Zhang (2011)
PEAR: Power efficiency through activity recognition (for ECG-based sensing)
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Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study
Siddika Parlak (2013)
Algorithm development and implementation of activity recognition system utilizing wearable MEMS sensors
Piyush Gupta (2014)
Realistic rendering of 3D avatar using physics simulation and minimal sensors
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Subject-dependent physical activity recognition using single sensor accelerometer
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Activity recognition using smartphone sensors
Alvina Anjum (2013)
Predicting length of stay at WiFi hotspots
Justin Manweiler (2013)
AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks
X. Qi (2013)
Wearable electric potential sensing: a new modality sensing hair touch and restless leg movement
Arash Pouryazdan (2016)
The Impact of Feature Vector Length on Activity Recognition Accuracy on Mobile Phone
Sulaimon A. Bashir (2015)
We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones
J. Hua (2017)
Kernel fusion based extreme learning machine for cross-location activity recognition
Zhelong Wang (2017)
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