Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization

Akhter, Israr; Jalal, Ahmad; Kim, Ki-Bum

doi:10.1007/s42835-021-00756-y

Cited by 48 publications

(18 citation statements)

References 24 publications

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“…Therefore, the equation of a Bezier curve with three control points is simplified as in Equation (9). Figure 5 shows the results of the Bezier curves fitting on the hand.…”

Section: Bezier Curvesmentioning

confidence: 99%

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Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.

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“…Therefore, the equation of a Bezier curve with three control points is simplified as in Equation (9). Figure 5 shows the results of the Bezier curves fitting on the hand.…”

Section: Bezier Curvesmentioning

confidence: 99%

“…The appliances that you use every day are an important part of your home. Consumers today are very careful about their comfort and safety and are more interesting in smart appliances [7][8][9]. They will facilitate their daily life by controlling the lights, speakers, air conditioners, and similar robots through hand gestures [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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“…Some example templates are presented in Figure 5. The sum of absolute differences (SAD) [51,52] is used to measure the intensities of pixels. A pixel in a search image with coordinates (x s , y s ) has an intensity I s (x s , y s ), and a pixel in a template image with coordinates (x t , y t ) has an intensity I t (x t , y t ) [53].…”

Section: People Verificationmentioning

confidence: 99%

A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

et al. 2021

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Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.

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“…Fortunately, in this area of research more features and methodologies are evolving along with a fusion of multi-sensors. Improvements in body-worn multi-sensors, such as electromyography (EMG), electroencephalography (EEG), accelerometer, gyroscope, magnetometer, and electrocardiography (ECG), have expanded research capacities and fortified the progress of new daily locomotion detection (DLD) applications [2], including online health services, recovery centers, safety observations, emergency facilities, healthcare assistance, and bio-feedback systems [3][4][5][6][7]. All these practices need regular check-ups and followups.…”

Section: Introductionmentioning

confidence: 99%

MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

et al. 2022

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More adaptable and user-independent techniques are required for multi-sensors based daily locomotion detection (MS-DLD). This research study proposes a couple of locomotion detection methods using body-worn multi-sensors to successfully categorize several locomotion transitions, including falling, walking, jogging, and jumping, along with bodyspecific sensors based on the modified hidden Markov models (HMMs) approach. This research presents both standard and state-of-the-art methods for MS-DLD. Conventionally, to improve MS-DLD process, the proposed methodology consists of a wavelet transformed Quaternion-based filter for the inertial signals, patterns recognition in the form of kinematic-static energies, and state-of-the-art multi-features extraction. These features include entropy, spectral, and cepstral coefficients domains. Then, fuzzy logic-based optimization has been introduced in order to achieve the selective features by converting them into codewords. This paper also introduces another state-of-the-art way to model daily locomotion detection and derives body-specific modified HMMs. The model divides the sensor data into three active body-specific parts including head sensors, mid-body sensors, and lower body sensors. Body-specific modified HMMs have been provided with raw data for the three active body-specific sensors and gave better results with less computational complexities when compared to the conventional methods. The proposed systems have been experimentally assessed and trialed over three diverse publicly available datasets: the UP-Fall dataset consisting of falling and other daily life activities, the IM-WSHA dataset comprising everyday locomotion actions, and the ENABL3S gait and locomotion dataset consisting of multiple gait movements. Experimental outcomes indicate that the proposed conventional technique achieved improved results and outperformed existing systems based on detection accuracies of 90.0% and 87.5% over UP-Fall, 86.0% and 88.3% over IM-WSHA, and 86.7% and 90.0% over ENABL3S datasets for kinematic and static energy patterns, respectively. Further, the results show that the state-of-the-art body-specific modified HMMs method achieved 94.3% and 95.0% over UP-Fall, 92.0% and 93.3% over IM-WSHA, and 90.0% and 95.0% over ENABL3S datasets for kinematic and static patterned signals, respectively. The results of state-of-theart efficient system show a significant increase in detection accuracy when compared to standard systems.

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Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization

Cited by 48 publications

References 24 publications

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

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