Journal ArticleDOI
Feature extraction from smartphone inertial signals for human activity segmentation
Rubén San-Segundo,Juan Manuel Montero,Roberto Barra-Chicote,Fernando Fernández,José Manuel Pardo +4 more
TLDR
Adapted MFCC and PLP coefficients improve human activity recognition and segmentation accuracies while reducing feature vector size considerably, overcome significantly baseline error rates and contribute significantly to reduce the segmentation error rate.About:
This article is published in Signal Processing.The article was published on 2016-03-01. It has received 83 citations till now. The article focuses on the topics: Scale-space segmentation & Feature extraction.read more
Citations
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Journal ArticleDOI
Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey
TL;DR: In this article, the authors focused on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors, which is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities.
Journal ArticleDOI
Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification
TL;DR: The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification.
Journal ArticleDOI
A Comprehensive Study of Activity Recognition using Accelerometers
Niall Twomey,Tom Diethe,Xenofon Fafoutis,Atis Elsts,Ryan McConville,Peter A. Flach,Ian J Craddock +6 more
TL;DR: This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers, particularly focused on long-term activity recognition in real-world settings.
Journal ArticleDOI
Robust Human Activity Recognition using smartwatches and smartphones
TL;DR: This work analyzes and proposes several techniques to improve the robustness of a Human Activity Recognition (HAR) system that uses accelerometer signals from different smartwatches and smartphones.
Journal ArticleDOI
Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors.
Luis Sigcha,Luis Sigcha,Nélson Costa,I. Pavón,Susana P. G. Costa,Pedro Arezes,Juan Manuel López,Guillermo de Arcas +7 more
TL;DR: A new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer is presented to enhance the FOG detection performance to be used in real home-environments and shows that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of Fog detection.
References
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Journal ArticleDOI
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
S. Davis,Paul Mermelstein +1 more
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
Book ChapterDOI
Activity recognition from user-annotated acceleration data
Ling Bao,Stephen S. Intille +1 more
TL;DR: 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, and suggests that multiple accelerometers aid in recognition.
Journal ArticleDOI
Perceptual linear predictive (PLP) analysis of speech
TL;DR: A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, which uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum, and yields a low-dimensional representation of speech.
Journal ArticleDOI
Activity recognition using cell phone accelerometers
TL;DR: This work describes and evaluates a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing, and has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity.