Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

By Henry Friday Nweke, Teh Ying Wah, Mohammed Ali Al-Garadi, Uzoma Rita Alo. Expert systems with applications, 2018

827 citations recorded by the source corpus.

Abstract Human activity recognition systems are developed as part of a framework to enable continuous monitoring of human behaviours in the area of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and surveillance in smart home environments. The extraction of relevant features is the most challenging part of the mobile and wearable sensor-based human activity recognition pipeline. Feature extraction influences the algorithm performance and reduces computation time and complexity. However, current human activity recognition relies on handcrafted features that are incapable of handling complex activities especially with the current influx of multimodal and high dimensional sensor data. With the emergence of deep learning and increased computation powers, deep learning and artificial intelligence methods are being adopted for automatic feature learning in diverse areas like health, image classification, and recently, for feature extraction and classification of simple and complex human activity recognition in mobile and wearable sensors. Furthermore, the fusion of mobile or wearable sensors and deep learning methods for feature learning provide diversity, offers higher generalisation, and tackles challenging issues in human activity recognition. The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition. The review presents the methods, uniqueness, advantages and their limitations. We not only categorise the studies into generative, discriminative and hybrid methods but also highlight their important advantages. Furthermore, the review presents classification and evaluation procedures and discusses publicly available datasets for mobile sensor human activity recognition. Finally, we outline and explain some challenges to open research problems that require further research and improvements.

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

View Paper: https://www.semanticscholar.org/paper/d0cf8271b76661b58936f443bfe7387d18ddf9a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Machine Learning at the Network Edge: A Survey (2019, methodology): “Augmentation helps ML models avoid overfitting issues by generating enough new training data [106].”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Discriminative deep architectures are specifically applied for supervised deep learning (Nweke et al. 2018).”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “An RNN integrates a temporal layer to capture sequential data and then learns multifaceted variations through the hidden units of the recurrent cell [192].”
Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things (2022, background): “The fast development of artificial intelligence (AI) provides new possibility for data analysis through machine learning (ML) to reinforce the application of TENGs in IoTs [26, 27].”
The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review (2023, methodology): “Deep learning (DL) algorithms are a subset of ML algorithms employing multi-layered artiﬁcial neural networks to identify intricate patterns and ﬁnd relationships within massive datasets [194].”
Deep Learning for Sensor-based Human Activity Recognition (2020, background): “ years. Very few works aim at giving a comprehensive review of current progress. Wang et al. [154] surveyed a number of deep learning methods for sensor-based human activity recognition. Nweke et al. [104] presented a survey on mobile and wearable sensor-based activity recognition and categorized th”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “However, the challenge exists where the produced new data samples must properly be distributed in a different class in order to attain maximum accuracy from ML algorithms [186].”
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning (2019, background): “• Human Activity Recognition Nweke et al. [41] proposed human activity recognition systems for the continuous monitoring of human behaviors in the environment.”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “There are some existing review papers in the literature for deep learning approaches for sensor-based human activity recognition [63, 204, 224, 291].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “A temporal layer is incorporated in an RNN for analyzing data sequentially followed by learning about multi-dimensional differences in unrevealed units of recurrent components [165].”
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, background): “…solutions have successfully achieved HAR recognition activities during the last decade, but they face certain challenges in certain cases like [7]–[9]. i Limited amount of training data for multiclass activity recognition ii Recognizing arbitrary and complex features using handcrafted/manual…”