A Survey on Human Activity Recognition using Wearable Sensors

By Oscar D. Lara, Miguel A. Labrador. IEEE Communications Surveys and Tutorials, 2013

2,631 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/8d3041129b500b90521c7d768996fc2de11b0e47

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Citations in the corpus

Driver-Activity Recognition in the Context of Conditionally Autonomous Driving (2015, background): “In addition to the detection of body-part motions, posture, and gaze direction based on computer vision methods, wearable sensors can provide additional and highly accurate data [10].”
A Survey on Wireless Body Area Networks: Technologies and Design Challenges (2014, background): “Surveys on mobile phone sensing and human activity recognition using wearable sensors are given in [17] and [18] respectively.”
Wireless Sensing for Human Activity: A Survey (2020, background): “Traditionally, it mainly relies on dedicated sensors, such as wearable devices [121] or cameras [122].”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, background): “Labrador [18] distinguish between seven groups of activities: (1) ambulation, (2) transportation, (3) phone usage, (4) daily”
Activity recognition with smartphone sensors (2014, background): “References [7, 8] are the most recent ones that both contribute on taxonomy in activity recognition.”
Review of Wearable Devices and Data Collection Considerations for Connected Health (2021, background): “Hybrid approaches are an efficient class of systems that intend to exploit both techniques [164].”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “intra-subject and inter-subject variability for the same activity, 3) the trade-off between performance and privacy, 4) computational efficiency in embedded and portable devices, and 5) difficulty of data annotation [3].”
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference (2018, background): “Activity recognition is an active area of research with multiple approaches depending on the application domain and the types of sensors [28].”
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey (2015, neutral): “In addition to the industry IoT solutions, academic solutions in the wearable computing area are discussed in [22] and [23].”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, methodology): “…use principal component analysis (PCA), Linear Discriminant Analysis (LDA), Independent component analysis (ICA), Factors Analysis (FA) and Minimum Description Length (MDL) to reduce redundant or irrelevant features in feature sets that can negatively affect the recognition accuracy [78–81].”
Generalizing DTW to the multi-dimensional case requires an adaptive approach (2016, neutral): “Providing accurate and exploitable information on human activity has become an active field in pervasive computing (Ding et al. 2008; Lara and Labrador 2013).”
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, background): “…can be easily obtained via built-in inertial sensors of wearable and mobile devices, activity recognition and classiﬁcation using smart devices are widely used to monitor, analyze, and understand one person’s status in different scenes across a variety of applications and systems [5], [6].”
A Survey of Online Activity Recognition Using Mobile Phones (2015, methodology): “There is a number of survey publications that have reviewed the work done so far in this area [1,14–17].”
A Survey of Selected Indoor Positioning Methods for Smartphones (2017, background): “For typical smartphone deployment scenarios it will be enough to identify the following modes of transit and activities [74], [83], [105]: idle, picking up and putting down the phone, texting, transitioning between standing and sitting, level walking, up- and downstairs climbing, riding on elevators”