Wavelets and subband coding
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Indoor Navigation Using a Diverse Set of Cheap, Wearable Sensors
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
Personal Position Measurement Using Dead Reckoning
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Validated caloric expenditure estimation using a single body-worn sensor
Proceedings of the 11th international conference on Ubiquitous computing
Analysis of Time and Frequency Domain Features of Accelerometer Measurements
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
Online gesture recognition for user interface on accelerometer built-in mobile phones
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Phase registration of a single quasi-periodic signal using self dynamic time warping
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Accelerometry-based classification of human activities using Markov Modeling
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Benchmarking the performance of SVMs and HMMs for accelerometer-based biometric gait recognition
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
Zee: zero-effort crowdsourcing for indoor localization
Proceedings of the 18th annual international conference on Mobile computing and networking
A reliable and accurate indoor localization method using phone inertial sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use. This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.