Detection of Basic Human Physical Activities With Indoor–Outdoor Information Using Sigma-Based Features and Deep Learning

Memis, Gokhan; Sert, Mustafa

doi:10.1109/jsen.2019.2916393

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Machine learning and opportunistic sensing based approach are explored in [19] and [20] respectively for monitoring humans about their actions. Other important researches found in the literature include feature extraction and deep learning [21], classification of sports and daily activities using ML models [22], deep learning for knowing human physical actions [23], monitoring of player activities in presence of mobility [24] and passive mobile sensing for continuous authentication [25].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone

Sangisetti¹,

Pabboju²

2023

IJACSA

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Personal Activity Recognition (PAR) is an indispensable research area as it is widely used in applications such as security, healthcare, gaming, surveillance and remote patient monitoring. With sensors introduced in smart phones, data collection for PAR made easy. However, PAR is non-trivial and difficult task due to bulk of data to be processed, complexity and sensor placement positions. Deep learning is found to be scalable and efficient in processing such data. However, the main problem with existing solutions is that, they could recognize up to 6 or 8 actions only. Besides, they suffer from accurate recognition of other actions and also deal with complexity and different placement positions of smart phone. To address this problem, in this paper, we proposed a framework named Robust Deep Personal Action Recognition Framework (RDPARF) which is based on enhanced Convolutional Neural Network (CNN) model which is trained to recognize 12 actions. RDPARF is realized with our proposed algorithm known as Enhanced CNN for Robust Personal Activity Recognition (ECNN-RPAR). This algorithm has provision for early stopping checkpoint to optimize resource consumption and faster convergence. Experiments are made with MHealth benchmark dataset collected from UCI repository. Our empirical results revealed that ECNN-RPAR could recognize 12 actions under more complex and different placement positions of smart phone besides outperforming the state of the art exhibiting highest accuracy with 96.25%.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone

Sangisetti¹,

Pabboju²

2023

IJACSA

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“…), the position of limbs, and legs including background details. Most of the human activity recognition published literature consists of supervised learning [ 26 , 27 ] and semi-supervised learning [ 28 ]. In the case of human activity recognition, the deep models require large training data; to tackle this problem, the transfer learning approach has been thoroughly studied [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sadiq

Ahn

Choi

2020

Sensors

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A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

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“…In a sports science context, inertial sensor data based on accelerometer and gyroscope signals are the most important source of movement analysis [17]. As the most commonly used sensor for acquiring human activity signals, inertial measurement units (IMUs) are widely used in sports recognition [18][19][20][21]. Liu et al [22] used a body sensor network (BSN) to collect motion data, and a support vector machine (SVM) was used to identify table tennis movements offline.…”

Section: Introductionmentioning

confidence: 99%

Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor

Xia

Wang

et al. 2020

Sensors

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Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.

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Detection of Basic Human Physical Activities With Indoor–Outdoor Information Using Sigma-Based Features and Deep Learning

Cited by 5 publications

References 39 publications

Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone

Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone

Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor

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