Three-layered hierarchical scheme with a Kinect sensor microphone array for audio-based human behavior recognition

Ding, Ing-Jr; Liu, Jianting

doi:10.1016/j.compeleceng.2015.03.032

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…Sound sensors, also known as microphones, detect and measure sound waves. Sound sensors have also been utilized in the field of human behavior recognition [ 49 , 50 , 51 ]. Acoustic data can be categorized into several behavioral patterns [ 49 ].…”

Section: Related Workmentioning

confidence: 99%

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A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

Maiti

Schmidt

et al. 2023

Sensors

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Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants’ privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person’s sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system’s performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.

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Section: Related Workmentioning

confidence: 99%

“…Sound sensors have also been utilized in the field of human behavior recognition [ 49 , 50 , 51 ]. Acoustic data can be categorized into several behavioral patterns [ 49 ]. These may include encouraging regular breaks during meetings or conference calls or encouraging individuals to stand or walk around while using the microphone.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

Maiti

Schmidt

et al. 2023

Sensors

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“…Internet of things (IoT) has been attracting increasing attention from the research community [2], [3]. In particular, the inter-connections among devices, buildings, sensors, etc.…”

Section: Applications To Iot Big Data and Disaster Managementmentioning

confidence: 99%

An investigation on human dynamics in enclosed spaces

Trovati

Asimakopoulou²,

Bessis

2018

Computers & Electrical Engineering

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In this article, we introduce a method for analysing specific dynamical properties associated with the movement of people on a two-dimensional (compact) space. We focus on a variety of features defined by the topology and dynamics of the system to investigate human dynamics in enclosed spaces. This can potentially have significant applications within systems defined by human behaviour, particularly relevant to disaster management, internet of things (IoT), and big data analytics.

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“…Speaker recognition using the user's spoken voice data for identity classification has been commonly used in practical applications (Ding and Shi, 2016;Ding and Yen, 2015;Ding et al, 2014;Ding and Liu, 2016). Active user recognition based on the active user's gesture command data is a new technical challenge.…”

Section: Introductionmentioning

confidence: 99%

Combinations of eigenspace and GMM with Kinect sensor-extracted action gesture features for person identity recognition

Ding

2016

Self Cite

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Purpose The Kinect sensor released by Microsoft is well-known for its effectiveness on human gesture recognition. Gesture recognition by Kinect has been proved to be an efficient command operation and provides an additional human-computer interface in addition to the traditional speech recognition. For Kinect gesture recognition in the application of gesture command operations, recognition of the active user making the gesture command to Kinect will be an extremely crucial problem. The purpose of this paper is to propose a recognition method for recognizing the person identity of an active user using combined eigenspace and Gaussian mixture model (GMM) with Kinect-extracted action gesture features. Design/methodology/approach Several Kinect-derived gesture features will be explored for determining the effective pattern features in the active user recognition task. In this work, a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of each individual classifier. In addition to Kinect-extracted feature designs for active user recognition, this study will further develop a combined recognition method, called combined eigenspace-GMM, which properly hybridizes the decision information of both the eigenspace and the GMM for making a more reliable user recognition result. Findings Active user recognition using an effective combination of eigenspace and GMM with well-developed active gesture features in Kinect-based active user recognition will have an outstanding performance on the recognition accuracy. The presented Kinect-based user recognition system using the presented approach will further have the competitive benefits of recognition on both gesture commands and providing users of gesture commands. Originality/value A hybridized scheme of eigenspace and GMM performs better than eigenspace-alone or GMM-alone on recognition accuracy of active user recognition; a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of the individual classifier; combined eigenspace-GMM active user recognition belonging to model-based active user recognition design has a fine extension on increasing the recognition rate by adjusting recognition models.

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Three-layered hierarchical scheme with a Kinect sensor microphone array for audio-based human behavior recognition

Cited by 16 publications

References 18 publications

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

An investigation on human dynamics in enclosed spaces

Combinations of eigenspace and GMM with Kinect sensor-extracted action gesture features for person identity recognition

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