A Survey on Human Activity Recognition and Classification

Gupta, Abhay; Gupta, Kuldeep; Gupta, Kshama; Gupta, Kapil

doi:10.1109/iccsp48568.2020.9182416

Cited by 51 publications

(27 citation statements)

References 14 publications

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“…It is seriously affected by poor light conditions or occlusion. In addition, it is easy for the camera to invade humans' privacy, and it is destined to be restricted in places with higher privacy, such as bedrooms and toilets [14]. It is obviously impractical to adopt the camera-based approach in the privacy-sensitive environment of certain rooms.…”

Section: Introductionmentioning

confidence: 99%

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Yin

Chen

Miao

et al. 2021

Sensors

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Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users’ privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.

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Section: Introductionmentioning

confidence: 99%

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Yin

Chen

Miao

et al. 2021

Sensors

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“…There is a myraid of human activity recognition algorithms in the literature [ 7 , 8 , 9 , 10 , 11 ] many of which can recognize complex activities from wearable sensors. Still, the accuracy and precision of the algorithms depend on the number of activities, activity type, and data collection available.…”

Section: Introductionmentioning

confidence: 99%

“…A system with precision is desirable for both high-performance systems and general monitoring systems in general since accurately detecting particular activities is of utmost importance. This is especially true in rehabilitation when whether or not a patient does particular activities and for how long (e.g., brushing your teeth, walking up stairs, walking down stairs) is highly relevant [ 10 , 11 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Classifying Physical Activities from Accelerometer Data

Johansen

Johannessen

Jiao

et al. 2021

Sensors

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Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

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“…To determine the best approach regarding multiple criteria, including cost, wearability, and classification performance, multiple surveys have been proposed in the literature. However, most studies have focused on determining the technologies and computational methods used for general human body motion [5].…”

Section: Introductionmentioning

confidence: 99%

“…To summarize the actual status of the literature, Table 1 presents 13 surveys relevant to understanding human motion based on inertial sensors. Reviewed Papers [5] 2020 Activity recognition methods Full body 8 [6] 2020 Classification of the position and number of inertial sensors Full body 58 [7] 2019…”

Section: Introductionmentioning

confidence: 99%

Using Inertial Sensors to Determine Head Motion—A Review

Severin

Dobrea

2021

J. Imaging

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Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

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A Survey on Human Activity Recognition and Classification

Cited by 51 publications

References 14 publications

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Deep Learning for Classifying Physical Activities from Accelerometer Data

Using Inertial Sensors to Determine Head Motion—A Review

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