Recognizing human motion with multiple acceleration sensors

By Jani Mäntyjärvi, Johan Himberg, Tapio Seppänen. 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236), 2001

385 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/190cad8ca9b67f13ccb171453cfad89ea143d703

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Citations in the corpus

Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey (2010, methodology): “Wavelet Coefficients Wavelet transforms divide the original signal into wavelet coefficients which enable the signal to be analyzed with a resolution matched to the resolution to the scale of coefficient[65].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Plenty of traditional hand-crafted feature-based approaches [57], [312], [313], [314], [316], [317], [318], [319], [320] have been proposed for HAR using acceleration data.”
Activity Recognition in the Home Using Simple and Ubiquitous Sensors (2004, neutral): “[19,11,18]).”
Sensor-Based Activity Recognition (2012, background): “As a result of the wave of intensive investigation, there have seen a plethora of impressive works on sensor-based activity recognition in the past several years [57], [58], [62], [64], [76]–[80], [82], [83], [85], [87], [97].”
A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer (2010, background): “Some of these investigated the use of acceleration signals to analyze and classify different types of the same physical activity, e.g., walking along the corridor, upstairs, and downstairs [3], [4].”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, methodology): “There are systems using single sensor accelerometer-based system [25], [39], [159], [160] and those fusing several sensors [63], [161]”
Window Size Impact in Human Activity Recognition (2014, background): “For example, changes between activities could be identiﬁed through the analysis of variations in the frequency characteristics.”
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, methodology): “Mantyjarvi et al. [21] utilized the independent component analysis and principal component analysis schemes to recognize one person’s walking posture based on the acceleration data collected from buttocks.”
Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers (2004, background): “Acceleration–based activity recognition has been studied by different research groups [11, 14, 19].”
An Activity Recognition System For Mobile Phones (2009, background): “Mantyjarvi et al. [ 16 ] also applied neural networks to human motion recognition.”
Activity recognition from acceleration data based on discrete consine transform and SVM (2009, methodology): “In this paper, an efficient method for high-accuracy activity recognition based on the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) is presented.”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “For example, Mantyjarvi et al (2001) preprocessed data from two tri-axial accelerometers using PCA and ICA, and used this as input to a Wavelet-based feature generation technique.”
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers (2010, background): “Frequency-domain features can be derived from the coefficients of time-frequency transforms, like the Short Time Frequency Transform (STFT), the Continuous or the Discrete Wavelet Transform (CWT, DWT) [32-34].”
Activity Recognition from User-Annotated Acceleration Data (2004, methodology): “Activity recognition has been performed on acceleration data collected from the hip (e.g. [17, 19]) and from multiple locations on the body (e.g. [5, 14]).”
Walk detection and step counting on unconstrained smartphones (2013, background): “Most studies use these features for training classifiers such as neural networks [29, 30, 39, 45], support vector machines (SVMs) [19, 41, 51] or Gaussian mixture models (GMMs) [2, 21, 57], or give them as an input to non-parametric classification methods such as k-nearest neighbour [45, 52].”