• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.141-148
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• 2010

In this paper, voice activity detection (VAD) for dual-channel noisy speech recognition is proposed in which spatial cues are employed. In the proposed method, a probability model for speech presence/absence is constructed using spatial cues obtained from dual-channel input signal, and a speech activity interval is detected through this probability model. In particular, spatial cues are composed of interaural time differences and interaural level differences of dual-channel speech signals, and the probability model for speech presence/absence is based on a Gaussian kernel density. In order to evaluate the performance of the proposed VAD method, speech recognition is performed for speech segments that only include speech intervals detected by the proposed VAD method. The performance of the proposed method is compared with those of several methods such as an SNR-based method, a direction of arrival (DOA) based method, and a phase vector based method. It is shown from the speech recognition experiments that the proposed method outperforms conventional methods by providing relative word error rates reductions of 11.68%, 41.92%, and 10.15% compared with SNR-based, DOA-based, and phase vector based method, respectively.

• BMB Reports
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• v.47 no.3
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• pp.149-157
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• 2014

The recent dramatic improvements in high-resolution mass spectrometry (MS) have revolutionized the speed and scope of proteomic studies. Conventional MS-based proteomics methodologies allow global protein profiling based on expression levels. Although these techniques are promising, there are numerous biological activities yet to be unveiled, such as the dynamic regulation of enzyme activity. Chemical proteomics is an emerging field that extends these types proteomic profiling. In particular, activity-based protein profiling (ABPP) utilizes small-molecule probes to monitor enzyme activity directly in living intact subjects. In this mini-review, we summarize the unique roles of smallmolecule probes in proteomics studies and highlight some recent examples in which this principle has been applied.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.629-642
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• 2013

Recently, with the advent of smart phones, it brought many changes in lives of modern people. Especially, application utilizing the sensor information of smart phone, which provides the service adapted by user situations, has been emerged. Sensor data of smart phone can be used for recognizing the user situation, Because it is closely related to the behavior and habits of the user. currently, GPS sensor one of mobile sensor has been utilized a lot to recognize basic user activity. But, depending on the user situation, activity recognition system cannot receive GPS signal, and also not collect received data. So utilization is reduced. In this paper, for solving this problem, we suggest a method of user activity recognition that focused on the accelerometer sensor data using smart phone. Accelerometer sensor is stable to collect the data and it's sensitive to user behavior. Finally this paper suggests a noble approach to use state transition diagrams which represent the natural flow of user activity changes for enhancing the accuracy of user activity recognition.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.321-344
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• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• Journal of the Korean Home Economics Association
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• v.41 no.10 s.188
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• pp.75-84
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• 2003

The purpose of this study was to examine the effect of listening to music during musical activities on children's development of tone recognition and sense of rhythm. The subjects were total sixty 4-years-olds from two classes of thirty. The children were divided into two groups, experimental and control. Before the experimental procedures, a pre-test was taken to evaluate the level of tone recognition and sense of rhythm of the children. Cordon's 'Audie' was employed and used to measure the difference of tone recognition and sense of rhythm. Then, the activity of listening to music was applied to the experimental group for ten weeks. For the experimental group, the musical activity was selected based on the themes of our tfe which was related to the weekly and yearly teaching plan. One the other hand, no musical activity was provided for the control group. After the experiment, a post-test was carried out using the same methodology of pre-test. Data were analysed by ANCOVA test. Results showed that there was a statistically significant difference in the development of tone recognition and sense of rhythm between the experimental group and the control group.

• Industry Promotion Research
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• v.9 no.2
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• pp.1-12
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• 2024

This study examines the role of Environment, Social, and Governance (ESG) activity recognition on job satisfaction, prosocial activities, and job performance among customer representatives working in call center environments. After gathering data from 264 call center workers in major South Korean insurance companies, the analysis w as performed using SmartPLS 4.0. This study's findings reveal that employee recognition of ESG activities significantly enhanced job satisfaction. The impact of ESG activity recognition on prosocial behavior was positive but relatively weak. Job satisfaction influences both prosocial behavior and the job performance of employees. Finally, prosocial behavior positively influences job performance. The most significant finding is that employees' recognition of companies' ESG management practices serves as a job resource. This recognition enhances employees' attitudes, behavior, and performance, signaling the potential benefits of informing employees about corporations' ethical behaviors.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.157-165
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• 2009

In this paper, we propose a distributed model that recognize ADLs of human can be occurred in daily living places. We collect and analyze user's environmental, location or activity information by simple sensor attached home devices or utensils. Based on these information, we provide a lifecare services by inferring the user's life pattern and health condition. But in order to provide a lifecare services well-refined activity recognition data are required and without enough inferred information it is very hard to build an ADL activity recognition model for high-level situation awareness. The sequence that generated by sensors are very helpful to infer the activities so we utilize the sequence to analyze an activity pattern and propose a distributed linear time inference algorithm. This algorithm is appropriate to recognize activities in small area like home, office or hospital. For performance evaluation, we test with an open data from MIT Media Lab and the recognition result shows over 75% accuracy.