Deep Learning for Sensor-based Activity Recognition: A Survey

By Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, Lisha Hu. 2017

1,854 citations recorded by the source corpus.

Abstract Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

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Citations in the corpus

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, methodology): “When applied to time-series classification-related HAR, the CNN has superiority over other conventional ML approaches, due to its local dependency and scale invariance [26].”
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, methodology): “The wearable devices, especially fitness trackers and smartwatches, are mostly used for recognition as it has various inbuilt/integrated sensors such as Accelerometer, Gyroscope, and Orientation sensors [3] available at preferable cost.”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, background): “With the recent success of deep learning, many healthcare applications adopt some representative networks including convolutional neural network, autoencoder, and have achieved satisfactory performances [35].”
LSTM-CNN Architecture for Human Activity Recognition (2020, background): “Human activity recognition (HAR) plays an important role in people’s daily lives because it has the ability to learn profound advanced knowledge about human activities from raw sensor data [1].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Activity recognition is another interesting area that relies on data collected by mobile motion sensors [384], [386].”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “There are some existing review papers in the literature for deep learning approaches for sensor-based human activity recognition [63, 204, 224, 291].”
A Comprehensive Survey of Vision-Based Human Action Recognition Methods (2019, background): “[35] summarized existing literature in the context of three aspects: sensor modality, deep models, and application.”
Recent trends in machine learning for human activity recognition—A survey (2018, methodology): “Initial research on HAR has considered HAR to be a conventional pattern recognition problem (Wang, Chen, Hao, Peng, & Hu, 2017).”
Deep learning for time series classification: a review (2018, methodology): “More recently in Wang et al. (2018), the architectures proposed in Wang et al. (2017b) were modified to leverage a filter initialization technique based on the Daubechies 4 Wavelet values (Rowe and Abbott, 1995).”
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare (2019, background): “Other applications include mental health assessment, fall detection, and sports monitoring.(3) In fact, there is a growing trend for wearable healthcare over the years.”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Some survey papers reviewed methods on sensor-based HAR [41], [42], [43], [44], [45], [46], [47], [48], [49].”