Activity Recognition from User-Annotated Acceleration Data

By Ling Bao, Stephen S. Intille. 2004

3,515 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physiology

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Citations in the corpus

Convolutional Neural Networks for human activity recognition using mobile sensors (2014, neutral): “Many existing AR approaches often rely on statistical features such as mean, variance, entropy or correlation coefficients [3].”
Human Activity Recognition from Accelerometer Data Using a Wearable Device (2011, background): “Even if first works about activity recognition used high dimensional and densely sampled audio and video streams [9], in many recent works ([ 2 ],[1]), activity recognition is based on classifying sensory data using one or many accelerometers.”
Simple and Complex Activity Recognition through Smart Phones (2012, methodology): “Bao and Intille [4] used a series of five biaxial accelerometers placed on the left bicep, right wrist, left quadriceps, right ankle, and right hip for recognizing twenty different activities ranging from walking to folding laundry to strength training.”
Activity recognition and monitoring using multiple sensors on different body positions (2006, methodology): “Decision Trees were used for activity classification in [1] and [2].”
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, background): “Moreover, human experience is speciﬁcally employed to derive handmade characteristics [35], such as statistical evidence, but refuses to differentiate between events with identical patterns such as standing and sitting behaviors.”
Using mobile phones to determine transportation modes (2010, methodology): “Various features including the mean, variance, energy, and the DFT (Discrete Fourier Transform) energy coef.cients between 1-10Hz based on magnitude of the force vector of the accelerometer were evaluated along with the speed of the GPS receiver [Bao and Intille 2004; Kern et al. 2003].”
A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009, methodology): “A recent study carried out by Bao and Intille [11] obtained high levels of classification accuracy using a mixed set of time and frequency-domain features.”
A robust human activity recognition system using smartphone sensors and deep learning (2018, background): “Bao and Intille [12] proposed one of the earliest HAR systems for the recognition of 20 activities of daily living using five wearable biaxial accelerometers and well-known machine learning classifiers.”
A Scalable Approach to Activity Recognition based on Object Use (2007, background): “In other approaches, information from accelerometers is used to identify actions such as walking and climbing stairs [1, 20].”
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals (2017, background): “[7] use multiple accelerometers on the hip, wrist, arm, ankle and thigh to classify 20 classes of everyday household activities.”
Fusion of Smartphone Motion Sensors for Physical Activity Recognition (2014, positive): “We used the sliding window approach with an overlap of 50% (1 s overlap here), based on the reporting of previous studies [3,34,35].”
Mobility Detection Using Everyday GSM Traces (2006, background): “A third approach to activity recognition is to use wearable sensors of a single modality [6] or multiple modalities [19].”
Distributed recognition of human actions using wearable motion sensor networks (2009, positive): “One particular problem associated with most distributed sensor systems is that each local observation from a distributed sensor is biased and insufﬁcient to classify all classes.”
Assessment of physical activity in youth. (2008, background): “More recently, activitytype classification schemes for interpretation of accelerometry data have emerged that aim to detect a multidimensional movement signature and assign membership to a (limited) set of activity types (7, 37, 48, 75, 79, 122).”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “tem of context-aware apps such as behavior-based authentication [31], [47], [58], [18], [78], [94], fitness monitoring based on activity tracking [5], [76], [57], speech translation [48], [61] and traffic/environmental monitoring [3], [64], [83], [86].”
Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning (2013, methodology): “Then, we will review multitask learning and highlight the novelties of this work.”
A tutorial on human activity recognition using body-worn inertial sensors (2014, background): “[2002], Bao and Intille [2004], and Lester et al.”