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Prof. Hee-Cheol Kim
College of AI Convergence/Institute of Digital Anti-aging Healthcare (IDA), Inje University, Gimhae, Korea

Basic Info

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Research Keywords & Expertise

0 Human Computer Interaction
0 Software Engineering
0 Aging Science
0 Applied Artificial Intelligence
0 Digital healthcare

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Short Biography

Hee-Cheol Kim received an MS degree in Computer Science from SoGang University, Korea, in 1991, and a PhD in Computer Science from Stockholm University, Sweden, in 2001. He is a professor in the Department of Computer Engineering at Inje University, Korea. His interests are in the areas of human computer interaction, software engineering, and u-healthcare. He has published more than 70 papers in these areas.

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Journal article
Published: 07 August 2021 in Healthcare
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With the development of mobile and wearable devices with biosensors, various healthcare services in our life have been recently introduced. A significant issue that arises supports the smart interface among bio-signals developed by different vendors and different languages. Despite its importance for convenient and effective development, however, it has been nearly unexplored. This paper focuses on the smart interface format among bio-signal data processing and mining algorithms implemented by different languages. We designed and implemented an advanced software structure where analysis algorithms implemented by different languages and tools would seem to work in one common environment, overcoming different developing language barriers. By presenting our design in this paper, we hope there will be much more chances for higher service-oriented developments utilizing bio-signals in the future.

ACS Style

Moon-Il Joo; Satyabrata Aich; Hee-Cheol Kim. Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages. Healthcare 2021, 9, 1016 .

AMA Style

Moon-Il Joo, Satyabrata Aich, Hee-Cheol Kim. Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages. Healthcare. 2021; 9 (8):1016.

Chicago/Turabian Style

Moon-Il Joo; Satyabrata Aich; Hee-Cheol Kim. 2021. "Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages." Healthcare 9, no. 8: 1016.

Journal article
Published: 08 May 2021 in Sustainability
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In the digital era, almost every system is connected to a digital platform to enhance efficiency. Although life is thus improved, security issues remain important, especially in the healthcare sector. The privacy and security of healthcare records is paramount; data leakage is socially unacceptable. Therefore, technology that protects data but does not compromise efficiency is essential. Blockchain technology has gained increasing attention as it ensures transparency, trust, privacy, and security. However, the critical factors affecting efficiency require further study. Here, we define the critical factors that affect blockchain implementation in the healthcare industry. We extracted such factors from the literature and from experts, then used interpretive structural modeling to define the interrelationships among these factors and classify them according to driving and dependence forces. This identified key drivers of the desired objectives. Regulatory clarity and governance (F2), immature technology (F3), high investment cost (F6), blockchain developers (F9), and trust among stakeholders (F12) are key factors to consider when seeking to implement blockchain technology in healthcare. Our analysis will allow managers to understand the requirements for successful implementation.

ACS Style

Satyabrata Aich; Sushanta Tripathy; Moon-Il Joo; Hee-Cheol Kim. Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach. Sustainability 2021, 13, 5269 .

AMA Style

Satyabrata Aich, Sushanta Tripathy, Moon-Il Joo, Hee-Cheol Kim. Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach. Sustainability. 2021; 13 (9):5269.

Chicago/Turabian Style

Satyabrata Aich; Sushanta Tripathy; Moon-Il Joo; Hee-Cheol Kim. 2021. "Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach." Sustainability 13, no. 9: 5269.

Journal article
Published: 04 May 2021 in Diagnostics
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Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

ACS Style

Ali Hussain; Hee-Eun Choi; Hyo-Jung Kim; Satyabrata Aich; Muhammad Saqlain; Hee-Cheol Kim. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics 2021, 11, 829 .

AMA Style

Ali Hussain, Hee-Eun Choi, Hyo-Jung Kim, Satyabrata Aich, Muhammad Saqlain, Hee-Cheol Kim. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics. 2021; 11 (5):829.

Chicago/Turabian Style

Ali Hussain; Hee-Eun Choi; Hyo-Jung Kim; Satyabrata Aich; Muhammad Saqlain; Hee-Cheol Kim. 2021. "Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques." Diagnostics 11, no. 5: 829.

Journal article
Published: 26 March 2021 in Cancers
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The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

ACS Style

Cho-Hee Kim; Subrata Bhattacharjee; Deekshitha Prakash; Suki Kang; Nam-Hoon Cho; Hee-Cheol Kim; Heung-Kook Choi. Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering. Cancers 2021, 13, 1524 .

AMA Style

Cho-Hee Kim, Subrata Bhattacharjee, Deekshitha Prakash, Suki Kang, Nam-Hoon Cho, Hee-Cheol Kim, Heung-Kook Choi. Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering. Cancers. 2021; 13 (7):1524.

Chicago/Turabian Style

Cho-Hee Kim; Subrata Bhattacharjee; Deekshitha Prakash; Suki Kang; Nam-Hoon Cho; Hee-Cheol Kim; Heung-Kook Choi. 2021. "Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering." Cancers 13, no. 7: 1524.

Journal article
Published: 22 February 2021 in Applied Sciences
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With the recent development of artificial intelligence and data mining technology, various and intelligent vital sign analysis technologies have been developed. Vital sign analysis algorithms and technologies are primarily developed using MATLAB and open source technologies, such as Python and R. The analysis algorithms developed with such programming languages can only be employed and run in their own respective development environments and, hence, are unfortunately not considered as platform independent. In that respect, the interoperability between development tools is needed to ensure efficiency in terms of development time and efforts and reusability between analysis technologies and algorithms developed in different languages. This paper presents the development of a vital sign analysis system that ensures interoperability, which leads to one common environment connecting different development platforms. To maintain the interoperability between MATLAB and R programming, we designed and implemented the Algorithm Block Broker (AB Broker). AB Broker is composed of AB Adapter and AB Broker. Here, the AB Broker uses AB Adapter to request execution of analysis algorithms developed in different languages, such as MATLAB, R, and Python. It also searches and runs the algorithm, helping implement the requested analysis technique. The AB Broker-based vital sign analysis system enables the integrated management of analysis and data mining technologies developed in different languages. From a developer’s point of view, therefore, it is convenient and efficient to develop techniques using existing different programming technologies.

ACS Style

Moon-Il Joo; Hee-Cheol Kim. A Vital Sign Analysis System Based on Algorithm Block Broker for Interoperability between Algorithm Development Tools. Applied Sciences 2021, 11, 1913 .

AMA Style

Moon-Il Joo, Hee-Cheol Kim. A Vital Sign Analysis System Based on Algorithm Block Broker for Interoperability between Algorithm Development Tools. Applied Sciences. 2021; 11 (4):1913.

Chicago/Turabian Style

Moon-Il Joo; Hee-Cheol Kim. 2021. "A Vital Sign Analysis System Based on Algorithm Block Broker for Interoperability between Algorithm Development Tools." Applied Sciences 11, no. 4: 1913.

Journal article
Published: 12 June 2020 in Diagnostics
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Parkinson’s Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson’s Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person’s body after it’s onset. Therefore, the early detection of Parkinson’s Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson’s Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson’s Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson’s Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic—Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.

ACS Style

Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network. Diagnostics 2020, 10, 402 .

AMA Style

Sabyasachi Chakraborty, Satyabrata Aich, Hee-Cheol Kim. Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network. Diagnostics. 2020; 10 (6):402.

Chicago/Turabian Style

Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 2020. "Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network." Diagnostics 10, no. 6: 402.

Journal article
Published: 07 February 2020 in Healthcare
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Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

ACS Style

Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks. Healthcare 2020, 8, 34 .

AMA Style

Sabyasachi Chakraborty, Satyabrata Aich, Hee-Cheol Kim. 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks. Healthcare. 2020; 8 (1):34.

Chicago/Turabian Style

Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 2020. "3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks." Healthcare 8, no. 1: 34.

Journal article
Published: 16 November 2019 in Applied Sciences
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The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.

ACS Style

Satyabrata Aich; Sabyasachi Chakraborty; Jong-Seong Sim; Dong-Jin Jang; Hee-Cheol Kim. The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning. Applied Sciences 2019, 9, 4938 .

AMA Style

Satyabrata Aich, Sabyasachi Chakraborty, Jong-Seong Sim, Dong-Jin Jang, Hee-Cheol Kim. The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning. Applied Sciences. 2019; 9 (22):4938.

Chicago/Turabian Style

Satyabrata Aich; Sabyasachi Chakraborty; Jong-Seong Sim; Dong-Jin Jang; Hee-Cheol Kim. 2019. "The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning." Applied Sciences 9, no. 22: 4938.

Validation study
Published: 30 September 2018 in Sensors
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One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.

ACS Style

Satyabrata Aich; Pyari Mohan Pradhan; Jinse Park; Nitin Sethi; Vemula Sai Sri Vathsa; Hee-Cheol Kim. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors 2018, 18, 3287 .

AMA Style

Satyabrata Aich, Pyari Mohan Pradhan, Jinse Park, Nitin Sethi, Vemula Sai Sri Vathsa, Hee-Cheol Kim. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors. 2018; 18 (10):3287.

Chicago/Turabian Style

Satyabrata Aich; Pyari Mohan Pradhan; Jinse Park; Nitin Sethi; Vemula Sai Sri Vathsa; Hee-Cheol Kim. 2018. "A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer." Sensors 18, no. 10: 3287.

Journal article
Published: 29 August 2018 in Pharmaceutics
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This study aims at developing and evaluating reconstitutable dry suspension (RDS) improved for dissolution rate, oral absorption, and convenience of use of poorly water-soluble celecoxib (CXB). Micro-sized CXB particle was used to manufacture nanosuspension by using bead milling and then RDS was made by spray-drying the nanosuspension with effective resuspension agent, dextrin. The redispersibility, morphology, particle size, crystallinity, stability, dissolution, and pharmacokinetic profile of the RDS were evaluated. RDS was effectively reconstituted into nanoparticles in 775.8 ± 11.6 nm. It was confirmed that CXB particles are reduced into needle-shape ones in size after the bead-milling process, and the description of CXB was the same in the reconstituted suspension. Through the CXB crystallinity study using differential scanning calorimetry (DSC) and XRD analysis, it was identified that CXB has the CXB active pharmaceutical ingredient (API)’s original crystallinity after the bead milling and spray-drying process. In vitro dissolution of RDS was higher than that of CXB powder (93% versus 28% dissolution at 30 min). Furthermore, RDS formulation resulted in 5.7 and 6.3-fold higher area under the curve (AUC∞) and peak concentration (Cmax) of CXB compared to after oral administration of CXB powder in rats. Collectively, our results suggest that the RDS may be a potential oral dosage formulation for CXB to improve its bioavailability and patient compliance.

ACS Style

Hye-In Kim; Sang Yeob Park; Seok Ju Park; Jewon Lee; Kwan Hyung Cho; Jun-Pil Jee; Hee-Cheol Kim; Han-Joo Maeng; Dong-Jin Jang. Development and Evaluation of a Reconstitutable Dry Suspension to Improve the Dissolution and Oral Absorption of Poorly Water-Soluble Celecoxib. Pharmaceutics 2018, 10, 140 .

AMA Style

Hye-In Kim, Sang Yeob Park, Seok Ju Park, Jewon Lee, Kwan Hyung Cho, Jun-Pil Jee, Hee-Cheol Kim, Han-Joo Maeng, Dong-Jin Jang. Development and Evaluation of a Reconstitutable Dry Suspension to Improve the Dissolution and Oral Absorption of Poorly Water-Soluble Celecoxib. Pharmaceutics. 2018; 10 (3):140.

Chicago/Turabian Style

Hye-In Kim; Sang Yeob Park; Seok Ju Park; Jewon Lee; Kwan Hyung Cho; Jun-Pil Jee; Hee-Cheol Kim; Han-Joo Maeng; Dong-Jin Jang. 2018. "Development and Evaluation of a Reconstitutable Dry Suspension to Improve the Dissolution and Oral Absorption of Poorly Water-Soluble Celecoxib." Pharmaceutics 10, no. 3: 140.

Review
Published: 07 December 2011 in Lecture Notes in Electrical Engineering
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Accelerometers are being increasingly used in studies of physical activity (PA) under a variety of circumstances, especially free-living environment. They can be used to assess a range of different aspects of PA, including energy expenditure, activity classification, gait, balance and fall. This paper reviews the use of accelerometers in these areas, along with the basic knowledge of accelerometers, preparatory work before data processing and the comparison of commonly-used products. The work of this review can provide a basis of accelerometer-used PA measurement and a contribution to further research and design.

ACS Style

Yao Meng; Hee-Cheol Kim. A Review of Accelerometer-Based Physical Activity Measurement. Lecture Notes in Electrical Engineering 2011, 223 -237.

AMA Style

Yao Meng, Hee-Cheol Kim. A Review of Accelerometer-Based Physical Activity Measurement. Lecture Notes in Electrical Engineering. 2011; ():223-237.

Chicago/Turabian Style

Yao Meng; Hee-Cheol Kim. 2011. "A Review of Accelerometer-Based Physical Activity Measurement." Lecture Notes in Electrical Engineering , no. : 223-237.