Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

Ignatov, Andrey

doi:10.1016/j.asoc.2017.09.027

Cited by 594 publications

(332 citation statements)

References 4 publications

Supporting

Mentioning

321

Contrasting

Unclassified

Order By: Relevance

“…Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize and assess activities but in smart homes, physical activities (i.e., running, cycling) cannot be performed due to the nature of activities.The most widely used sensors for recording physical activities data using a smartphone are the accelerometer, gyroscope, and position sensor [8,[14][15][16][17][18][19][20]]. An accelerometer is capable of tracking activity readings to infer complex user motions, such as tilt, swing, or rotation.Researchers showed that the accelerometer sensor is the most reliable and cheapest alternate of wearable sensors for physical activity recognition [19,[21][22][23].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

show abstract

mentioning

confidence: 99%

“…The most widely used sensors for recording physical activities data using a smartphone are the accelerometer, gyroscope, and position sensor [8,[14][15][16][17][18][19][20]]. An accelerometer is capable of tracking activity readings to infer complex user motions, such as tilt, swing, or rotation.…”

mentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Deep Learning methods gained popularity in recent years due to its ability for automatic feature extraction [20]. Many deep learning algorithms have been used for HAR such as CNN ( [8], [9], [13], [24]), RNN( [10], [12], [21]), Autoencoders [25]. Most of these algorithms suffer from problem of requirement of high computation power, making them difficult to be deployed on edge devices.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Mean cross entropy is used as cost function between ground truth and predicted labels. Adam optimizer is used to minimize cost function and update model parameters [24]. This model is trained on Raspberry Pi3 to check the ability of model to work on edge device.…”

Section: Trainingmentioning

confidence: 99%

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

Agarwal

Alam

2020

Procedia Computer Science

View full text Add to dashboard Cite

Human Activity Recognition (HAR) using wearable and mobile sensors has gained momentum in last few years, in various fields, such as, healthcare, surveillance, education, entertainment. Nowadays, Edge Computing has emerged to reduce communication latency and network traffic. Edge devices are resource constrained devices and cannot support high computation.In literature, various models have been developed for HAR. In recent years, deep learning algorithms have shown high performance in HAR, but these algorithms require lot of computation making them inefficient to be deployed on edge devices. This paper, proposes a Lightweight Deep Learning Model for HAR requiring less computational power, making it suitable to be deployed on edge devices. The performance of proposed model is tested on the participant's six daily activities data. Results show that the proposed model outperforms many of the existing machine learning and deep learning techniques.

show abstract

“…Yang et al proposed a simple method for identifying human activities based on simple object information involved in RFID usage activities [8]. Andrey et al proposed an accelerometer-based convolutional network for activity recognition [9]. However, it is inconvenient for users to carry, most users are not willing to carry the sensor on their body, and the acquisition of activity sometimes depends on factors such as the location carried by the sensor.…”

Section: Related Workmentioning

confidence: 99%

Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes

Guo

Xiong

et al. 2019

Complexity

View full text Add to dashboard Cite

Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents' lives with minimal cost, daily activity recognition aims to know resident's daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition.

show abstract

Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

Cited by 594 publications

References 4 publications

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes

Contact Info

Product

Resources

About