• PNF and Movement
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• v.13 no.1
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• pp.39-46
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• 2015

Purpose: The accelerometer is a tool for evaluating walking by the displacement of the center of mass (COM) in the body. Recently, smartphones have added an accelerometer app, and it can be used to evaluate outcomemanures in rehabilitation. The purpose of this study was to investigate the COM in the bodies of normal persons and stroke patients using this smartphone application while walking. Methods: Twenty normal persons and twenty-two stroke patients were recruited and had their COM measured using G-walk and the smartphone application, SMAP, during 10 m walking. Subjects repeated the 10 m of walking 3 times, and we used the SMAP, Accelerometer Monitor ver. 1.5.0, to evaluate COM during the walk. To measure the displacement of COM, we used the difference in value between the maximal angle and the minimum anterior-posterior (AP), mediolateral (ML), and rotational angles during the walk. Results: For the normal persons, there was significant correlation between the AP and AP of SMAP, and was also a significant correlation between rotational angle and the ML of SMAP. In the stroke patients, there was significant correlation between AP and ML, and the rotational angle of SMAP. Conclusion: Our research results suggest that if the SMAP system is reinforced in the case of patients who have a greater displacement of COM, it may be used as an evaluation tool during walking.

• Nutrition Research and Practice
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• v.16 no.5
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• pp.565-576
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• 2022

BACKGROUND/OBJECTIVES: Various accelerometer equations are used to predict energy expenditure (EE). On the other hand, the development of these equations and their validation studies have been conducted primarily without including older adults. This study assessed the accuracy of 8 ActiGraph accelerometer equations to predict the energy cost of walking in older adults. SUBJECTS/METHODS: Thirty-one participants with a mean age of 74.3 ± 3.3 yrs were enrolled in this study (20 men and 11 women). The participants completed 8 walking activities, including 5 treadmill and 3 self-paced walking activities. The EE was measured using a portable indirect calorimeter, with each participant simultaneously wearing the ActiGraph accelerometer. Eight ActiGraph equations were assessed for accuracy by comparing the predicted EE with indirect calorimetry results. RESULTS: All equations resulted in an overall underestimation of the EE across the activities (bias -1 to -1.8 kcal·min^-1 and -0.7 to -1.8 metabolic equivalents [METs]), as well as during treadmill-based (bias -1.5 to -2.9 kcal·min^-1 and -0.9 to -2.1 METs) and self-paced (bias -1.2 to -1.7 kcal·min-1 and -0.2 to -1.3 METs) walking. In addition, there were higher rates of activity intensity misclassifications, particularly among vigorous physical activities. CONCLUSIONS: The ActiGraph equations underestimated the EE for walking activities in older adults. In addition, these equations inaccurately classified the activities based on their intensities. The present study suggests a need to develop ActiGraph equations specific to older adults.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.990-992
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• 2007

The purpose of this study is to develop a practical gait-event detection system which is necessary for the FES (functional electrical stimulation) control of locomotion in paralyzed patients. The system is comprised of a sensor board and an event recognition algorithm. We focused on the practicality improvement of the system through 1) using accelerometer to get the angle of shank and dispensing with the foot-switches having limitation in indoor or barefoot usage and 2) using a rule-base instead of threshold to determine the heel-off/heel-strike events corresponding the stimulation on/off timing. The sensor signals are transmitted through RF communication and gait-events was detected using the peaks in shank angle. The system could detect two critical gait-events in all five paralyzed patients. The standard deviation of the gait events time from the peaks were smaller when 1.5Hz cutoff frequency was used in the derivation of the shank angle from the acceleration signals.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.21-26
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• 2017

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.215-220
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• 2014

Most of studies on wave height algorithms that are using at buoys describe algorithms using double integral to determine the position data from the acceleration data measured from the accelerometer. but sometimes, it can involve some cumulative error in that process, and result in misjudgment or unstabe system. On the other hand, It is widely known that the motion of fluid particles on or underneath a linear progressive wave is periodic and elliptic. This fact is considered in this article and leads a improved algorithms with no integral processing.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.5
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• pp.1-8
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• 2012

A Smart phone is the one of the mobile devices widely used in our daily life. Moreover, various type of sensing data gathering from smart phone are effectively applicable to recognize of their users or smart phone status. Therefore, many smart phone applications based on sensor data have been actively developed. In this paper, we investigate an unexpected conditions recognition method using continuous sensing data from a single three-axis accelerometer. In addition, we implemented an application using the proposed method which provides the services notifying an abrupt changes of the smart phone conditions. By the experimented results, the application can be useful to protect the smart phone on the user's unaware conditions such as falling or a robbery case.

• Journal of Sensor Science and Technology
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• v.18 no.6
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• pp.432-440
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• 2009

This paper presents an accelerometer based system for physical activity decision that are capable of recognizing three different types of physical activities, i.e., standing, walking and running, using by rough sets. To collect physical acceleration data, we developed the body sensor node which consists of two custom boards for physical activity monitoring applications, a wireless sensor node and an accelerometer sensor module. The physical activity decision is based on the acceleration data collected from body sensor node attached on the user's chest. We proposed a method to classify physical activities using rough sets which can be generated rules as attributes of the preprocessed data and by constructing a new decision table, rules reduction. Our experimental results have successfully validated that performance of the rule patterns after removing the redundant attribute values are better and exactly same compare with before.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2546-2549
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• 2002

이족 로봇(A biped robot)의 안정된 보행과 움직임을 구현하기 위해서는 정밀 센서의 접목이 필수 사항이다. 센서의 정보를 종합한 다음 보행 및 움직임에 적용함으로써 로봇은 향상된 독립성과 자율성을 가지게 되고 그로 인해 지능형 로봇에 한층 더 접근할 수 있게된다. 본 논문에서는 이족로봇의 안정된 보행을 위해 기본이 되는 자세 기울어짐을 측정할 수 있는 가속도 센서를 이용한 이족로봇의 제어 방법을 다루고자 한다. 본 논문의 로봇은 소형 R/C servo motor를 사용하여 설계, 제작 하였으며, 하드웨어 시스템은 메인 CPU로 인텔사의 80C296SA50을 사용, 가속도 측정센서로는 Analog Device 사의 Accelerometer ADXL210를 사용하였다. 이와 같이 가속도 센서를 사용한 시스템은 로봇의 자세를 측정, 판단을 가능케 하여 실시간으로 로봇의 자세를 안정되게 보정 할 수 있어 외부의 변화되는 힘에 자율적으로 대처할 수 있다. 이 때문에 더욱 안정된 지능형 이족로봇을 구현할 수 있다.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.2
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• pp.155-162
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• 2016

The purpose of this study was to determine the walking imbalance using the EMG(electromyogram). To confirm the effectiveness of the proposed encoder and acceleration, EMG sensor based gait imbalance determination system. This experiment was carried out to evaluation with a healthy adult male to 10 people. The Encoder device is attached to the hip and knee joint in order to measure the gait signal. The Accelerometer sensors are attached on the ankle. The EMG sensors are attached on the vastus lateralis and anterior tibialis. SI(Symmetry Index) was used as an index for determining the gait imbalance. To confirm if the judgment has been made correctly, the heel, regarded as the cause of unbalanced ambulation, was adjusted from 0 cm to 6 cm with intervals of 1.5 cm. In the cases of the encoder and the EMG, the difference of 0 cm and 1.5 cm is determined into normal walk but the other difference is distinguished into gait imbalance. In the case of the accelerometer, the difference of 0 cm, 1.5 cm and 3 cm is determined into normal walk but the other difference is distinguished into gait imbalance.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.49-56
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• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.