A Public Domain Dataset for Human Activity Recognition using Smartphones

By Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz. 2013

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Citations in the corpus

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019, methodology): “…our proposed method on six datasets, such as objects, text, digits, biological and sensor, namely STl-10 (Coates et al. , 2011), MNIST (LeCun et al. , 1998), HAR (Anguita et al. , 2013), Reuters (Lewis et al. , 2004), Non Line of Sight (Tancik et al. , 2018) and Diabetic Retinopathy (Graham, 2015).”
Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates (2019, background): “In response, there is a trend towards moving computation, including the training of machine-learning models, from cloud/datacenters to edge devices (Anguita et al., 2013; Pantelopoulos & Bourbakis, 2010).”
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019, methodology): “We present results on real datasets that are naturally collected at the user level: Human Activity Recognition With Smartphones (Harws, K = 30, D = 561) (Anguita et al., 2013), Vehicle Sensor (Duarte and Hu, 2004) (K = 23, D = 100), Computer Buyers (K = 190, D = 14) and School (Goldstein, 1991) (K =”
Federated Multi-Task Learning (2017, methodology): “Examples of potential applications include: learning sentiment, semantic location, or activities of mobile phone users; predicting health events like low blood sugar or heart attack risk from wearable devices; or detecting burglaries within smart homes [3, 37, 40].”
Federated Learning: Challenges, Methods, and Future Directions (2019, background): “Examples of potential applications include: learning sentiment, semantic location, or activities of mobile phone users; adapting to pedestrian behavior in autonomous vehicles; and predicting health events like heart attack risk from wearable devices [6, 52, 84].”
Provable defenses against adversarial examples via the convex outer adversarial polytope (2017, background): “HAR We present results on a human activity recognition dataset (Anguita et al., 2013).”
Prototypal Analysis and Prototypal Regression (2017, background): “The smartphone-based human activities recognition data set from Anguita et al. (2013) and Reyes-Ortiz et al. (2016) contains activity data collected by smartphone’s inertial sensors.”
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation (2019, methodology): “A CNN with two stacked convolution layers and one fully connected layer [20], [21] is applied on the MNIST handwritten digit recognition data set, and a LSTM with two stacked LSTM layers and one fully connected layer is chosen to accomplish the human activity recognition task [22], [23].”
Feature relevance quantification in explainable AI: A causality problem (2019, methodology): “es. Then, approximate g(T) by g cond(T) := P K i=1 w T(x;~x i)f(xi T;x T) P K i=1 w T(x;~xi) : For the experiment, we use the real data set Human Activity Recognition Using Smartphones Data Set (see Anguita et al. (2013)) from the UCI repository. The data set 8 consists of 561 features with a traini”
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints (2019, methodology): “We validate this observation via experiments on the GENKI (Whitehill and Movellan, 2012), HAR (Anguita et al., 2013) and COMPAS (ProPublica, 2016) datasets using the two GAP architectures presented on the left of Figure 2.”
Deep Recurrent Neural Networks for Human Activity Recognition (2017, methodology): “Table 1 summarizes the experimental datasets and the following are brief descriptions of them: 1) UCI-HAD [19]: Dataset for activities of daily living (ADL) recorded by using a waist-mounted smartphone with an embedded 3-axis accelerometer, gyroscope, and magnetometer.”
Eavesdrop the Composition Proportion of Training Labels in Federated Learning (2019, background): “, human activity recognition [1], heart rate monitoring [4] and keyboard prediction [2, 3]), but also in other fields, such as healthcare industry (e.”
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022, background): “Scenario 3irce a ir (HAR → GESTURE): Datasets record different activities (6 types of human daily activities vs. 8 hand gestures).”
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors (2017, background): “lization are used to prevent gradient vanishing or explosion as well as overshooting the learning updates. 4. Experiments We tested the Deep-Res-Bidir-LSTM network with the public domain UCI data set [24] and the Opportunity data set [7]. Then, we compared it with the outcomes of other methods and a”
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015, background): “Among the crowdsensing systems cited in the previous section ( [9]–[13], [15]–[19], [30], [31]), only [10], [12], [18] provide privacy mechanisms, of which only [10] addresses the privacy more formally.”
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020, background): “Three of them, MHEALTH [29], PAMAP2 [30] and UCI HAR [1], are public benchmarked datasets on activity recognition.”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, methodology): “For the experiments presented in this paper, we resampled the signal in order to have a constant sampling rate of 50 Hz, which is commonly used in literature for activity recognition from data acquired through smartphones [24, 23, 25].”