J. Hicks, N. Ramanathan, et. al. (2010).
AndWellness: An Open Mobile System for Activity and Experience Sampling. In Poster Session 2010 mHealth Summit.
J. Yang (2009). Toward physical activity diary: motion recognition
using simple acceleration features with mobile phones. In ICME '09
Proceedings of the 1st International Workshop on Interactive Multimedia
for Consumer Electronics.
T. Brezmes, J.L. Gorricho and J. Cotrina (2009). Activity Recognition from
Accelerometer Data on Mobile Phones. In IWANN '09: Proceedings of the
10th International Work-Conference on Artificial Neural Networks, 796-799.
Researchers implemented a real time system for classifying six basic daily
activities using a mobile phone containing an accelerometer. No server
processing data was involved. The K-nearest neighbors algorithm was used,
with the intent that users could train the device to detect their motions
for whatever location the particular user normally carries his or her
N. Gyorbiro, A. Fabian and G. Homanyi (2008).
An Activity Recognition System for Mobile Phones. In Mobile Networks
and Applications , 14:82-91.
Researchers used feed-forward backpropogation neural networks to distinguish
between six different motion patterns using data collected from three MotionBands
attached to the dominant wrist, hip, and ankle of each subject. Each
MotionBand contained a tri-axial accelerometer, magnetometer, and gyroscope.
A smartphone collected data from the MotionBand sensors.
N. Krishnan, D. Colbry, et. al. (2008).
Real Time Human Activity Recognition Using Tri-Axial Accelerometers. In Sensors
Signals and Information Processing Workshop.
Researchers designed a real time system for identifying five lower body
activities with data from 3 subjects collected from tri-axial accelerometers.
Accelerometers were placed on the right ankle and the left thigh. Researchers
extracted statistical and spectral features from the data, including mean,
variance, energy, spectral entropy, and correlation between the data of
all axes, and the AdaBoost algorithm built on decision stump for
classification was trained with three-fold cross validation, and, in
addition, probability of classifications were calculated.
N. Krishnan and S. Panchanathan (2008). ?Analysis of Low Resolution
Accelerometer Data for Continuous Human Activity Recognition. In IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP
Researchers evaluated the performance of different
classifiers on a subset of the accelerometer data used in the Bao and Intille
experiment. Classifiers included AdaBoost, SVM, and Regularized Logistic
Regression (RLogReg). Ten random subjects and seven lower body activities
were examined. The continuous acceleration stream was divided into fixed
length frames, and each frame was classified. Statistical features including
mean, variance, correlation between all the axis of the accelerometers
and spectral features including energy and entropy were calculated.
Y. Cho, Y. Nam, et. al. (2008). SmartBuckle: Human Activity Recognition
using a 3-axis Accelerometer and a Wearable Camera. In HealthNet,
Researchers used the SmartBuckle device for medical monitoring to recognize
each of 9 different activities. The device contained a tri-axial accelerometer
as well as an image sensor. Correlation between axes and the magnitude of
the FFT were used as features for the accelerometer data.
E.M. Tapia, S.S. Intille, et al. (2007).
Real-Time Recognition of Physical Activities and Their Intensities Using
Wireless Accelerometers and a Heart Rate Monitor . In Proceedings of
the 2007 11th IEEE International Symposium on Wearable Computers, 1-4.
Researchers used five triaxial accelerometers to collect data in real-time
from 21 users while the users performed thirty different gymnasium activities.
Several of these "activities" involved performing the same activity at
different levels of intensity. Using C4.5 DT and the Naive Bayes classifiers
in WEKA, researchers found that they could achieve high accuracies for activity
recognition for subject dependent analysis but much lower accuracies for
subject independent analysis. Adding in a heart rate monitor only slightly
improved the results, and combining activities that were the same activity
being performed at different intensities also improved accuracies slightly
U. Maurer, A. Smailagic, et. al. (2006).
Activity Recognition and Monitoring Using Multiple Sensors on Different Body
Positions. In IEEE Proceedings of the International Workshop on Wearable
and Implantable Body Sensor Networks,3(5):113-116.
Researchers sought to identify users? activities in real time using
?eWatch? sensors placed on the belt, shirt pocket, trouser pocket,
backpack, and necklace. Features from the accelerometer axes, the light
sensor, and a combined value of the accelerometer signals were calculated.
The Correlation-based Feature Selection (CFS) method from WEKA was used to
find feature sets that were highly correlated with a particular class but
uncorrelated with each other.
N. Ravi, N. Dandekar, et. al. (2005).
Activity Recognition from Accelerometer Data. In Proceedings of the
Seventeenth Conference on Innovative Applications of Artificial
Researchers collected data from a triaxial accelerometer
worn at subjects' waists. Mean, standard deviation, energy, and correlation
features were extracted from the data. In addition to analyzing the
performance of base-level classifiers like decision tables, decision trees,
K-nearest neighbors, SVM, and Naive Bayes, meta-level classifiers such as
boosting, bagging, plurality voting, stacking using ODTs, and stacking using
MDTs were applied to classify windows as one of eight daily activities.
L. Bao and S. Intille (2004).
Activity Recognition from User-Annotated Acceleration Data . In PERVASIVE, LNCS 3001, 1?17.
Five biaxial accelerometers placed on the right hip, dominant wrist,
non-dominant upper arm, dominant ankle, and non-dominant thigh were used to
collect data from 20 subjects. Twenty daily activities were considered. Mean
energy, frequency-domain entropy, and correlation of acceleration data were
calculated, and C4.5, instance-based learning, decision tables,
and Naive Bayes classifiers in WEKA were tested using these features.
M. Mathie, B. Celler et. al. (2004).
of Basic Daily Movements Using a Triaxial Accelerometer. In Medical
& Biological Engineering & Computing, 42:679-687.
Researchers developed their own binary tree for classification of basic
daily activities and created algorithms to describe each of these
activities. Using this model, they tested data collected from 26
subjects using a triaxial accelerometer attached at the subjects? waists.
Of particular interest in this study was the detection of falls.