Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

Alhammad, Nora; Al-Dossari, Hmood

doi:10.3390/app11062633

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…In the sliding window, sensing data collected from an inertial sensor are divided into small segments using a window size. The influence of window size on identification performances is intelligible [ 29 , 30 ]. However, no clear definition exists for the selection of the optimal window size in activities identification.…”

Section: Methodsmentioning

confidence: 99%

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Hsieh

Huang²,

Liu

et al. 2021

Sensors

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Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.

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Section: Methodsmentioning

confidence: 99%

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Hsieh

Huang²,

Liu

et al. 2021

Sensors

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“…Each fragment corresponds to a specific activity. In this study, the dynamic segmentation method proposed in our previous work [ 31 ] was applied. Unlike previous methods, this method is concerned with the segmentation of physical activities belonging to the same category (i.e., dynamic activities).…”

Section: Methodsmentioning

confidence: 99%

Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

Alhammad¹,

Al-Dossari²

2021

Sensors

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The research area of activity recognition is fast growing with diverse applications. However, advances in this field have not yet been used to monitor the rehabilitation of individuals with spinal cord injury. Noteworthily, relying on patient surveys to assess adherence can undermine the outcomes of rehabilitation. Therefore, this paper presents and implements a systematic activity recognition method to recognize physical activities applied by subjects during rehabilitation for spinal cord injury. In the method, raw sensor data are divided into fragments using a dynamic segmentation technique, providing higher recognition performance compared to the sliding window, which is a commonly used approach. To develop the method and build a predictive model, a machine learning approach was adopted. The proposed method was evaluated on a dataset obtained from a single wrist-worn accelerometer. The results demonstrated the effectiveness of the proposed method in recognizing all of the activities that were examined, and it achieved an overall accuracy of 96.86%.

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“…In the past few decades, various advanced computational methods have been applied in various fields of study such as chemical engineering [32][33][34][35][36][37], electrical and computer engineering [38][39][40][41], civil engineering [42][43][44], mechanical engineering [45][46][47][48][49][50][51], petroleum engineering [52][53][54][55][56][57][58][59][60][61][62][63], and environmental engineering [64,65], etc. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

Cited by 17 publications

References 36 publications

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

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