Strikes-Thrusts Activity Recognition Using Wrist Sensor Towards Pervasive Kendo Support System

Takata, Masashi; Nakamura, Yugo; Torigoe, Yohei; Fujimoto, Manato; Arakawa, Yutaka; Yasumoto, Keiichi

doi:10.1109/percomw.2019.8730861

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Therefore, we considered the use of an inertial measurement unit (IMU) as an alternative approach for contact identification. IMUs have been widely used in skill assessment in the field of sports [17], [18], [19] as well as the medical field such as surgery [20]. IMUs can allow recognition and measurement of minute motion.…”

Section: Purposementioning

confidence: 99%

Evaluation of Hand-Scaling Skills of Dental Hygienist Students: Identification of Contact Between Hand-Scaler Blade Tip and Tooth Surface

et al. 2022

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Dental hygienist students require a self-learning simulator to learn the correctness of handscaling techniques. An essential technique in hand scaling is the maintenance of contact between the tip of the hand-scaler blade and the tooth. However, imaging-based methods cannot effectively reveal this contact because the gingiva and buccal mucosa conceal the blade. Therefore, this study aimed to propose a method to identify the appropriate contact state of the blade with the tooth by using an inertial measurement unit (IMU) attached to the hand scaler and a force sensor attached to the target tooth. The hand-scaling motion was measured in an experiment in which participants were instructed to use the tip or middle of the blade to contact the tooth. The contact state of the blade, whether it was the tip or the middle, was identified using 18 features, including the average and standard deviation of nine dimensions of force, acceleration, and angular velocity with a support vector machine (SVM). The results showed that the model using all 18 features could classify the contact state with an accuracy of 97.1%. Furthermore, the accuracy was 95.9% with the 12 features from IMU alone, which was not significantly different from the accuracy with 18 features. The accuracy was 88.8% with six features from the force sensor alone. These results indicate that the IMU alone can identify the correct contact state, highlighting the possibility of creating a realistic simulator for training dental hygienists in evaluating the blade-contact state.

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Section: Purposementioning

confidence: 99%

Evaluation of Hand-Scaling Skills of Dental Hygienist Students: Identification of Contact Between Hand-Scaler Blade Tip and Tooth Surface

et al. 2022

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“…The IMUs are normally composed of a 3-axis accelerometer and a 3-axis gyroscope, and sometimes a 3axis magnetometer. IMUs have been proven to be reliable and accurate for many body movement measurement tasks [8], [9]. Based on the number and type of the IMUs, these sensing systems can be further divided into three categories: single IMU based systems, dual-IMU based systems and multi-IMU based systems.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the other movement detection studies [9], [44], this research used the majority voting scheme to label the data [45]. This is because other low-level movement studies instruct the participant to finish one movement "B" within a time frame and label the entire data chunk in this time frame as "B" without considering the actual time consumed for performing this movement [9].…”

Section: Data Processingmentioning

confidence: 99%

On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach

et al. 2021

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Lumbar-pelvic movements (LPMs) are generally performed in the clinical setting to identify limitations in a range of movements. Continuous monitoring of these movements can provide real-time feedback to both patients and medical experts with the potential of identifying activities that may precipitate symptoms of low back pain (LBP) as well as improving therapy by providing a personalised approach. Recent advances in mobile computing technology and wearable sensors have paved the way for developing mobile physical activity monitoring applications with more advanced and complex algorithms, such as deep neural network (DNN) based models. However, there is a lack of prior studies that focus on real-time LPMs detection with multimodal sensory data. Meanwhile, most research in the area of body movement detection do not consider the potential transition logic of the constituent low-level body movements (e.g., LPMs) within their corresponding high-level physical activity. This information could significantly increase accuracy of detection results. Further, current studies mainly perform deep learning-based movement detection in the cloud (or a backend server) that could increase network bandwidth and response time.To address these limitations, this paper proposes a novel LPMs detection approach using an enhanced and adapted hybrid DNN model, which includes a convolutional neural network followed by a long-short term memory recurrent network (CNN-LSTM), and performs detection locally on the mobile device. The results of a comparative evaluation of the proposed model and baseline models are described. We also introduce a set of domain-specific pre-defined rules, based on the transition logic information, to reconstruct the detection outputs to further improve the detection accuracy.

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“…Takata et al [ 6 ] instrumented the Shinai with two inertial measurement units (IMUs; and the Kendoka with further IMUs) for activity analysis and classification of scoring targets. Yotani et al [ 7 ] instrumented a Shinai with strain gauges and compared the deflection of Shinai to the muscle activation patterns.…”

Section: Introductionmentioning

confidence: 99%

Pressure Sensors for Measuring the Grip Pressure during Kendo Attacks: Assessment of Laterality and Evidence of the Five Phases of Attack

Jeong

Tan

Asai

et al. 2023

Sensors

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In Kendo, there is no consensus as to which hand should produce more pressure when attacking the opponent with the bamboo sword, let alone how to teach the pressure distribution during coaching. There is the theory that a Kendo attack can be divided into five phases, which has not entered the coaching practice, either. The aim of this study was to measure the grip pressure during Kendo attacks, investigate the pressure distribution between the two hands, and find evidence for the existence of the alleged five attack phases. We instrumented a bamboo sword with grip pressure sensors and investigated the grip pressure in 23 participants. In all attack targets and in both hands, the pressure across all attack phases was significantly different. In general, the left-hand pressure was consistently and significantly higher than the right-hand one, across all five attack phases, for the hand, head, and flank attack targets. The surprising exception was the throat target with only two attack phases, the strike phase of which showed a greater pressure in the right hand. Across all participants, the left-hand pressure was greater in 60.22–100% in any phase of the four attack targets, except for the strike phase of the throat target. Through these results, we could verify the effect of the teaching customs in Kendo, as well as provide first-time evidence of the existence of the five attack phases.

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Strikes-Thrusts Activity Recognition Using Wrist Sensor Towards Pervasive Kendo Support System

Cited by 9 publications

References 10 publications

Evaluation of Hand-Scaling Skills of Dental Hygienist Students: Identification of Contact Between Hand-Scaler Blade Tip and Tooth Surface

Evaluation of Hand-Scaling Skills of Dental Hygienist Students: Identification of Contact Between Hand-Scaler Blade Tip and Tooth Surface

On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach

Pressure Sensors for Measuring the Grip Pressure during Kendo Attacks: Assessment of Laterality and Evidence of the Five Phases of Attack

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