On automatizing recognition of multiple human activities using ultrasonic sensor grid

Ghosh, Arindam; Sanyal, Anubrata; Chakraborty, Amartya; Sharma, Praveen Kumar; Saha, Mousumi; Nandi, S.; Saha, Sujoy

doi:10.1109/comsnets.2017.7945440

Cited by 30 publications

(15 citation statements)

References 5 publications

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“…Radar sensor [47] Doppler radar-based fall detection system Eco Ultrasonic [48] Automated system for monitoring human activity using array of heterogeneous ultrasonic sensors Ultrasounds Hybrid [49] Fusing camera and accelerometer data Images and aceleration…”

Section: Technology Authors Proposal Signalmentioning

confidence: 99%

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

2022

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The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.

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Section: Technology Authors Proposal Signalmentioning

confidence: 99%

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

2022

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“…The same human action can be measured by various sensor types, but the pool of actions is wide, which makes an action-based comparison more difficult. Therefore, in order to make a more easily comparison across sensors, we make the sensor classification based on the physical measures and provide related applications with this type of sensor used in the sub-domains of HAR, such as indoor localization [14]- [19], home behavior analysis [20]- [26], quantified-self [27]- [29], gestures, postures recognition [30]- [37] and sensing of physiological signals [32], [38]- [41].…”

Section: Sensorsmentioning

confidence: 99%

“…Notably, for recognizing simple indoor activities, pulsed ultrasonic sensors are often used to measure distance towards the interacting object. Ghosh et al [27], [46] mounted 4 HC-SR04 sensors to cover a square of 70 cm x 70 cm with a LV-MaxSonar-EZ0 in the middle to reduce the dead zone. Based on the distance profile, they used the support vector machine (SVM), k nearest neighbours (k-NN), and Decision Tree approaches to classify the targeted activities.…”

Section: ) Ultrasonic Sensorsmentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

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Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people's everyday life. In order to solve many human-centered problems, such as health care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization. INDEX TERMS Sensor categorization, human activity recognition, public databases for human activity recognition, physical sensors, sensor benchmark.

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“…Takahiro et al (2017) [8] improved the accuracy of human activity recognition using ensemble learning based on single inertial measurement unit sensors. Ghosh et al (2017) [9] used ultrasound sensor arrays to identify human activities. In addition to the expansion of sophisticated sensors, the authors in [10] In our work, we refer to the method of extracting behavioural patterns in [7] and identify six actions, namely, running, walking, climbing up, climbing down, static and unknown.…”

Section: B Activity Recognitionmentioning

confidence: 99%

Individual Commute Time Recognition Based on the Hierarchical Semantic Model

et al. 2020

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Individual commute time recognition is essential for traffic demand management. However, this problem has yet to be studied. In this study, we propose a hierarchical semantic model (HSM) to recognize individual commute time. To the best of our knowledge, this work is the first to integrates large scale travellers commute time prediction at an individual level. HSM consists of a low and a high semantic layer. The low semantic layer models spatial, temporal and environmental information, whereas the high semantic layer recognises commute time using the hidden Markov model on the basis of the low semantic layer outputs. Experimental results demonstrate the effectiveness of our proposed model for individual commute time recognition. INDEX TERMS commute time recognition, hierarchical semantic model, traffic demand management

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On automatizing recognition of multiple human activities using ultrasonic sensor grid

Cited by 30 publications

References 5 publications

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Individual Commute Time Recognition Based on the Hierarchical Semantic Model

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