Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

By Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, Paul J. M. Havinga. ARCS, 2010

546 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/107f4901f88d566ced6b4399b2f7c962ff282d55

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Citations in the corpus

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016, background): “, from simple hand gestures to complex activities, such as “cooking a meal”) and the context in which they occur from sensor data is at the core of smart assistive technologies, such as in smart homes [1], in rehabilitation [2], in health support [3,4], in skill assessment [5] or in industrial setti”
UP-Fall Detection Dataset: A Multimodal Approach (2019, background): “Features References mean frequency [35,37] median frequency [35] entropy [36,40] energy [34,36,40] principal frequency [38–40] spectral centroid [37,40] One the other hand, each ZIP-file contains a set of compressed CSV-files that represents the relative displacement of pixels in two consecutive ima”
Challenges in multimodal gesture recognition (2016, background): “Different surveys have been published so far reviewing gesture recognition systems (LaViola Jr., 1999; Mitra and Acharya, 2007; Chaudhary et al., 2011; Ibraheem and Khan, 2012; Avci et al., 2010; Khan and Ibraheem; Kausar and Javed, 2011).”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “[42] introduced five major steps for the HAR process using inertial sensors, which include pre-processing, segmentation, feature extraction, dimensionality reduction, and classification.”
Using Inertial Sensors for Position and Orientation Estimation (2017, unknown): “A separate but related field of research is that of activity recognition using inertial sensors, where the aim is to determine the type of motion that is performed by a person wearing or carrying a device containing inertial sensors, see [19, 4] for an overview.”
A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications (2018, background): “As a result, there has been a shift in healthcare services from healthcare facilities to home environments [1].”
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, background): “Two main aspects of analyzing signals are time domain and spectral domain [14].”
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, background): “…interaction techniques [Kim et al. 2012], situated support in smart environments [Hoey et al. 2011], automated health [Plötz et al. 2012] and wellbeing assessments [Kranz et al. 2012], health care automation [Avci et al. 2010], sports tracking and coaching [Ladha et al. 2013] to name but a few.”
Transfer Learning for Activity Recognition: A Survey (2013, background): “Although recent progress in transfer learning has been analyzed in [50, 61, 70] and several surveys have been conducted on activity recognition [2, 4, 10, 24] no one has specifically looked into the intersection of these two areas.”
Walk detection and step counting on unconstrained smartphones (2013, background): “This work has appeared in various domains including medical [3], context-aware computing [15], biometric identification [1] and PDR [17]—each with different goals and methodologies.”