• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.159-166
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• 2009

The purpose of this study was to develop and automatic gait event detection algorithm using single accelerometer which is attached at the top of the shoe. The sinal vector magnitude and anterior-posterior(x-axis) directional component of accelerometer were used to detect heel strike(HS) and toe off(TO), respectively. To evaluate proposed algorithm, gait event timing was compared with that by force plate and kinematic data. In experiment, 7 subjects performed 10 trials level walking with 3 different walking conditions such as fast, preferred & slow walking. An accelerometer, force plate and 3D motion capture system were used during experiment. Gait event by force plate was used as reference timing. Results showed that gait event by accelerometer is similar to that by force plate. The distribution of differences were spread about $22.33{\pm}17.45m$ for HS and $26.82{\pm}14.78m$ for To and most error was existed consistently prior to 20ms. The difference between gait event by kinematic data and developed algorithm was small. Thus it can be concluded that developed algorithm can be used during outdoor walking experiment. Further study is necessary to extract gait spatial variables by removing gravity factor.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.131-136
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• 2007

In this study, we developed an active ankle-foot orthosis(AAFO) which can control dorsi/ plantar flexion of the ankle joint to prevent foot drop and toe drag during walking. 3D gait analyses were performed on five healthy subjects under three different gait conditions: the normal gait without AFO, the SAFO gait with the conventional plastic AFO, and the AAFO gait with the developed AFO. As a result, the developed AAFO preeminently induced the normal gait compared to the SAFO. Additionally, AAFO prevented foot drop by proper plantarflexion during loading response and provided enough plantarflexion moment as a driving force to walk forward by sufficient push-off during pre-swing. AAFO also could prevent toe drag by proper dorsiflexion during swing phase. These results indicate that the developed AAFO may have more clinical benefits to treat foot drop and toe drag, compared to conventional AFOs, and also may be useful in patients with other orthotic devices.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.977-978
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• 2013

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.55-62
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• 2004

In this paper, we have developed a portable acceleration measurement system to measure acceleration signals during walking and a gait analysis algorithm which can evaluate gait regularity and symmetry and estimate gait parameters automatically. Portable acceleration measurement system consists of a biaxial accelerometer, amplifiers, lowpass filter with cut-off frequency of 16Hz, one-chip microcontroller, EEPROM and RF(TX/RX) module. The algerian includes FFT analysis, filter processing and detection of main peaks. In order to develop the algorithm, eight hemiplegic patients for training set and the other eight hemiplegic patients for test set are participated in the experiment. Acceleration signals during 10m walking were measured at 60 samples/sec from a biaxial accelerometer mounted between L3 and L4 intervertebral area. The algorithm, detected foot contacts and classified right/left steps, and then calculated gait parameters based on these informations. Compared with video data and analysis by manual, algorithm showed good performance in detection of foot contacts and classification of right/left steps in test set perfectly. In the future, with improving the reliability and ability of the algerian so that calculate more gait Parameters accurately, this system and algerian could be used to evaluate improvement of walking ability in hemiplegic patients in clinical practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.10
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• pp.452-457
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• 2006

In this paper, an algorithm to detect steps in hemiplegic patients using a 3-axis accelerometer a紅ached on the trunk was proposed. The proposed algorithm consisted of the signal pre-processing, the step detector, the classification of steps and the calculation of stride time. Two FIR band-pass filters were designed and steps were measured by the combination of filtered signals in the vertical and the anteroposterior directions. In addition, the classification of steps and the calculation of stride time were computed by using the detected steps and lateral signals. For the experiment, fourteen hemiplegic patients were participated and the linear accelerations of the trunk and foot switch signals were measured synchronously. To evaluate the system performance, the detected steps and initial contacts by the foot switch were compared. The average error between the steps and initial contacts was 0.024ms and the difference of the average stride time was 0.01s. Finally, all gait events were detected exactly. Results showed that the accelerometry could use for the gait evaluation in clinical rehabilitation therapies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.547-548
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• 2006

• Physical Therapy Korea
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• v.9 no.3
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• pp.47-65
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• 2002

This study examined the correlations among the Berg Balance Scale, which is a clinical tool used to evaluate balance ability, spatiotemporal parameters of gait, and falling; determined the parameters most closely related to falling; and identified a discriminatory parameter and its predictability. Thirty-four subjects aged 72 to 92 years participated in this study. Following a questionnaire survey about falling, the Berg Balance Scale and spatiotemporal parameters of gait were measured. The results revealed that the incidence of falls increased with aging and an accompanying reduction in the flexion range of motion of the hip joint. The gait characteristics of elderly people who fell easily included a slower walking speed, shorter stride, and longer stance time than other elderly. When the cutoff score was set at 45, the Berg Balance Scale was able to identify correctly those individuals who truly have experience of falling than when the cutoff score was set at 39. But when the cutoff score was set at 39, the scale's specificity identifying correctly those individuals who truly have not experience of falling was higher than at the cutoff score of 45. Therefore, the Berg Balance Scale is an appropriate screening method in a clinical setting for the early detection of elderly people at risk of falling. In conclusion, elderly people with a Berg Balance Scale score. below 45 are the most likely to fall owing to their decreased balance ability.