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Hyo Sung Joo
Interdisciplinary Program of Medical and Biological Engineering, University of Ulsan, 93, Daehak-ro, Nam-gu, Ulsan 44610, Korea;(J.L.);(H.J.)

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Journal article
Published: 08 January 2020 in Sensors
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Without expert coaching, inexperienced exercisers performing core exercises, such as squats, are subject to an increased risk of spinal or knee injuries. Although it is theoretically possible to measure the kinematics of body segments and classify exercise forms with wearable sensors and algorithms, the current implementations are not sufficiently accurate. In this study, the squat posture classification performance of deep learning was compared to that of conventional machine learning. Additionally, the location for the optimal placement of sensors was determined. Accelerometer and gyroscope data were collected from 39 healthy participants using five inertial measurement units (IMUs) attached to the left thigh, right thigh, left calf, right calf, and lumbar region. Each participant performed six repetitions of an acceptable squat and five incorrect forms of squats that are typically observed in inexperienced exercisers. The accuracies of squat posture classification obtained using conventional machine learning and deep learning were compared. Each result was obtained using one IMU or a combination of two or five IMUs. When employing five IMUs, the accuracy of squat posture classification using conventional machine learning was 75.4%, whereas the accuracy using deep learning was 91.7%. When employing two IMUs, the highest accuracy (88.7%) was obtained using deep learning for a combination of IMUs on the right thigh and right calf. The single IMU yielded the best results on the right thigh, with an accuracy of 58.7% for conventional machine learning and 80.9% for deep learning. Overall, the results obtained using deep learning were superior to those obtained using conventional machine learning for both single and multiple IMUs. With regard to the convenience of use in self-fitness, the most feasible strategy was to utilize a single IMU on the right thigh.

ACS Style

Jaehyun Lee; Hyosung Joo; Junglyeon Lee; Youngjoon Chee; Lee. Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach. Sensors 2020, 20, 361 .

AMA Style

Jaehyun Lee, Hyosung Joo, Junglyeon Lee, Youngjoon Chee, Lee. Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach. Sensors. 2020; 20 (2):361.

Chicago/Turabian Style

Jaehyun Lee; Hyosung Joo; Junglyeon Lee; Youngjoon Chee; Lee. 2020. "Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach." Sensors 20, no. 2: 361.

Journal article
Published: 28 February 2017 in Sensors
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Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

ACS Style

Hyo Sung Joo; Su-Hyun Han; Jongshill Lee; Dong Pyo Jang; Joong Koo Kang; Jihwan Woo. Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures. Sensors 2017, 17, 481 .

AMA Style

Hyo Sung Joo, Su-Hyun Han, Jongshill Lee, Dong Pyo Jang, Joong Koo Kang, Jihwan Woo. Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures. Sensors. 2017; 17 (3):481.

Chicago/Turabian Style

Hyo Sung Joo; Su-Hyun Han; Jongshill Lee; Dong Pyo Jang; Joong Koo Kang; Jihwan Woo. 2017. "Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures." Sensors 17, no. 3: 481.

Journal article
Published: 12 June 2014 in Sensors
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It is important and recommended to detect hearing loss as soon as possible. If it is found early, proper treatment may help improve hearing and reduce the negative consequences of hearing loss. In this study, we developed smartphone-based hearing screening methods that can ubiquitously test hearing. However, environmental noise generally results in the loss of ear sensitivity, which causes a hearing threshold shift (HTS). To overcome this limitation in the hearing screening location, we developed a correction algorithm to reduce the HTS effect. A built-in microphone and headphone were calibrated to provide the standard units of measure. The HTSs in the presence of either white or babble noise were systematically investigated to determine the mean HTS as a function of noise level. When the hearing screening application runs, the smartphone automatically measures the environmental noise and provides the HTS value to correct the hearing threshold. A comparison to pure tone audiometry shows that this hearing screening method in the presence of noise could closely estimate the hearing threshold. We expect that the proposed ubiquitous hearing test method could be used as a simple hearing screening tool and could alert the user if they suffer from hearing loss.

ACS Style

Youngmin Na; Hyo Sung Joo; Hyejin Yang; Soojin Kang; Sung Hwa Hong; Jihwan Woo. Smartphone-Based Hearing Screening in Noisy Environments. Sensors 2014, 14, 10346 -10360.

AMA Style

Youngmin Na, Hyo Sung Joo, Hyejin Yang, Soojin Kang, Sung Hwa Hong, Jihwan Woo. Smartphone-Based Hearing Screening in Noisy Environments. Sensors. 2014; 14 (6):10346-10360.

Chicago/Turabian Style

Youngmin Na; Hyo Sung Joo; Hyejin Yang; Soojin Kang; Sung Hwa Hong; Jihwan Woo. 2014. "Smartphone-Based Hearing Screening in Noisy Environments." Sensors 14, no. 6: 10346-10360.