Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

By Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz. 2012

982 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning privately from multiparty data (2016, methodology): “To test the algorithms, we use the UCI Human Activity Recognition Dataset (Anguita et al., 2012), which is a collection of motion sensor data on a smart device by multiple subjects performing 6 activities (walking, walking upstairs, walking downstairs, sitting, standing, laying).”
A robust human activity recognition system using smartphone sensors and deep learning (2018, methodology): “In [34], the authors used a smartphone mounted in the waist to collect inertial sensors’ data for activity recognition.”
Transition-Aware Human Activity Recognition Using Smartphones (2016, methodology): “Similarly, we proposed in [27] a HAR system using a waist-mounted smartphone.”
Fusion of Smartphone Motion Sensors for Physical Activity Recognition (2014, methodology): “The SVM classifier is implemented on smartphones in [20,42].”
Deep Learning Models for Real-time Human Activity Recognition with Smartphones (2019, methodology): “In [42, 43], a smartphone mounted at the waist was used to collect data from inertial sensors for activity recognition.”
Smartphone Sensors for Health Monitoring and Diagnosis (2019, methodology): “8 • Activities were classified using supervised machine learning (SVM, Decision tree, KNN and discriminant analysis) algorithms.”
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks (2015, methodology): “To improve the accuracy, the pervious work tried to extract effective handcrafted features from tri-axis signals of accelerometers and gyroscopes [1] or explored different classifiers including Support Vector Machine [5], Random Forest [6] and Hidden Markov Model [7].”
Soft Threshold Weight Reparameterization for Learnable Sparsity (2020, background): “These devices, including mobile phones (Anguita et al., 2012) and IoT sensors (Patil et al.”
A Public Domain Dataset for Human Activity Recognition using Smartphones (2013, methodology): “underlining that the MC-SVM model outperforms by 7% the classifier learned on our previous dataset described in [19], where only acceleration data from the smartphone were taken into account for the recognition: this suggests that the new features, introduced in the publicly available dataset as dep”
Machine Learning at the Network Edge: A Survey (2019, methodology): “designed a new algorithm, Multiclass Hardware Friendly Support Vector Machine (MC-HF-SVM), for building models geared towards edge-devices, focusing on healthcare applications such as human activity recognition and monitoring [11].”
Activity recognition with smartphone sensors (2014, background): “Anguita et al.[31] proposed a hardware-friendly multi-class SVM for a smartphone activity recognition application that is used in healthcare.”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, background): “Images of common materials used for training material recognition systems; HAR (har) [40].”
Federated Learning for Healthcare Informatics (2019, background): “Activity recognition [16] CNN 5 UCI Smartphone [4]”
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “The increasing popularity of smartphones and smartwatches, and the advances in their sensing and computing power, promotes using them as convenient and natural agents for context recognition systems [6], [18], [19], [20], [21], [22], [24], [26], [27], [31], [32].”