Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors

Chen, Chen; Liu, Kui; Jafari, Roozbeh; Kehtarnavaz, Nasser

doi:10.1109/embc.2014.6944534

Cited by 31 publications

(4 citation statements)

References 9 publications

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“…It is demonstrated experimentally that the proposed framework permits reliable and unobtrusive fall detection in real-time and at low computational cost. In [49], a fitness test measurement system for seniors based depth camera information and a low-cost wearable inertial sensor data was introduced. It was experimentally proved that by utilizing the signals from these two differing modality sensors in a collaborative manner, the measurements associated with senior fitness test can be obtained with high rates of success under realistic conditions.…”

Section: Multimodal Sensor Fusionmentioning

confidence: 99%

Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Ahmad

2024

Preprint

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<p>Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.</p> <p>While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.</p>

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Section: Multimodal Sensor Fusionmentioning

confidence: 99%

Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Ahmad

2024

Preprint

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“…Human Action Recognition (HAR) has recently gained a lot of research attention due to its integrated nature in numerous applications such as human-computer interface (HCI) [1], motion analysis, intelligent monitoring [2], virtual reality, and some computer vision-related applications like intelligence surveillance [3][4][5] and content-based video retrieval. Applying HAR enables a better understanding of people's actions and habits through video monitoring and pattern observation.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Rao,

Bhagyaraju

et al. 2024

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“…The main purpose of HAR based on vision is to process and analyze the original image or image sequence data collected by the sensor (camera) via computer, to learn and understand the human action and behavior. HAR based on computer vision technology has been extensively used in several fields of human life, such as smart video surveillance [ 1 , 2 ], human-machine interaction [ 3 ], robotics [ 3 ], video analytics [ 4 ], and human activity recognition [ 5 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

Tang

Tong

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.

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Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors

Cited by 31 publications

References 9 publications

Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

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