• Journal of the Korean Society of Safety
• /
• v.30 no.3
• /
• pp.93-99
• /
• 2015

Economic burden of work-related musculoskeletal disorder(WMDs) is increasing. Known causes of WMDs include improper posture, repetition, load, and temperature of workplace. Among them, improper postures play an important role. A smart sensor called SensorTag is employed to estimate the trunk postures including flexion-extension, lateral bend, and the trunk rotational speeds. Measuring gravitational acceleration vector in the smart sensor along the tri-orthogonal axes offers an orientation of the object with the smart sensor attached to. The smart sensor is light in weight and has small form factor, making it an ideal wearable sensor for body posture measurement. Measured data from the smart senor is wirelessly transferred for analysis to a smartphone which has enough computing power, data storage and internet-connectivity, removing need for additional hardware for data post-processing. Based on the estimated body postures, WMDs risks can be conviently gauged by using existing WMDs risk assesment methods such as OWAS, RULA, REBA, etc.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.1205-1208
• /
• 2014

본 논문은 항상 지니고 있는 스마트폰으로 노트북 사용자가 마우스를 휴대해야 하는 불편함을 해결하기 위해 이를 대체할 수 있는 안드로이드용 스마트폰 마우스 어플리케이션을 설계하였다. 우리는 기존의 다른 연구나 실제 이미 출시된 마우스 어플리케이션들과 달리, 블루투스를 이용하여 인터넷을 사용할 수 없는 상황에서도 작동하도록 하였으며 터치패드가 아닌 가속도 센서를 통해 실제 마우스처럼 동작을 인식하도록 하였다. 또한, 스마트폰 어플리케이션의 장점인 추가적인 기능을 쉽게 추가할 수 있다는 점을 살려서 진동 모드, 자동 연사, dpi 조절 기능 등을 포함한 게이머 마우스로 설계하였다. 정확한 마우스 포인터의 이동을 표현하기 위해 운동방정식으로 센서값을 속도로 바꿔서 이를 사용하였다. 그리고 센서 오차로 인한 오류 막기 위해 센서값을 필터링하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.551-554
• /
• 2015

본 논문에서는 스마트워치의 3축 가속도 센서와 스마트폰의 기압센서를 이용한 행동 인식 시스템을 제안한다. 스마트워치에서 획득한 3축 가속도 값을 수직, 수평 성분으로 추출하고, 스마트폰에서 획득한 기압센서의 차이를 추출하여 행동을 인식하였다. 실험 결과에서 3축 가속도 센서 기반의 행동 인식률은 66.62%를 보였으나 제안한 3축 가속도 센서와 기압센서를 이용한 행동인식률은 95.45%를 보였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.385-388
• /
• 2012

본 논문에서는 스마트 폰에 내장된 3축 가속도 센서를 이용해 제스처 훈련 및 테스터 데이터를 수집하고, DTW(Dynamic Time Warping) 알고리즘을 근간으로 하는 효과적인 제스처 인식 방법을 제안한다. 본 논문에서 제안하는 제스처 인식 방법의 성능을 분석하기 위해 안드로이드 스마트 폰에서 동작하는 제스처 인식 프로그램을 개발하였고, 이것을 이용해 수행한 성능실험 결과를 소개한다.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.8
• /
• pp.95-103
• /
• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.221-228
• /
• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1514-1517
• /
• 2010

최근 출시된 IPhone이나 Android Phone들을 보면 인터페이스의 편리성과 엔터테이먼트적인 요소의 극대화를 위한 가속도센서, 디지털 나침반, 근접센서, 조도센서 등, 다양한 센서들을 디바이스에 포함하고 있다. 이들 중 가속도 센서를 이용한 다양한 인터페이스가 여러 스마트폰 게임과 어플리케이션들에서 사용되고 있는데 본 논문에서는 가속도 센서의 각축의 값들을 이용해 문자나 특정 입력 값을 기기에 전달할 수 있는 제스처 인식을 위한 센서 값들의 효율적인 사전 보정 알고리즘에 대해서 제안하고자 한다.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.4
• /
• pp.286-291
• /
• 2020

In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user's activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.

• Journal of Internet of Things and Convergence
• /
• v.10 no.4
• /
• pp.101-108
• /
• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.3
• /
• pp.35-42
• /
• 2012

The database for driving patterns can be utilized in various system such as automatic driving system, driver safety system, and it can be helpful to monitor driving style. Therefore, we propose a driving pattern recognition system in which the sensor streams from a smartphone are recorded and used for recognizing driving events. In this paper we focus on the driving pattern recognition that is an essential and preliminary step of driving style recognition. We divide input sensor streams into 7 driving patterns such as, Left-turn(L), U-turn(U), Right-turn(R), Rapid-Braking(RB), Quick-Start(QS), Rapid-Acceleration (RA), Speed-Bump(SB). To classify driving patterns, first, a preprocessing step for data smoothing is followed by an event detection step. Last the detected events are classified by DTW(Dynamic Time Warping) algorithm. For assisting drivers we provide the classified pattern with the corresponding video stream which is recorded with its sensor stream. The proposed system will play an essential role in the safety driving system or driving monitoring system.