The gesture recognition technology based on IMU sensor for personal active spinning

Kang, Mi-Seon; Kang, Hyunwoo; Lee, Cheolhyo; Moon, Kiyoung

doi:10.23919/icact.2018.8323825

Cited by 7 publications

(9 citation statements)

References 2 publications

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“…Kang et al [ 40 ] developed a gesture recognition technology for spinning exercise in which the user used a spin bike to move the upper body to upper, lower, left, right and wave. They collected movement data from the user’s wrist and head using an inertial measurement unit (IMU) sensor then processed the information with a gesture recognition module and classified gestures into nine classes.…”

Section: Categorizationmentioning

confidence: 99%

“…In paper [ 30 , 68 ], they utilized an SVM to analyze position and motion from Kinect data. Researchers also classified gestures using data from wearable devices utilizing an SVM [ 40 ].…”

Section: Technical Attributesmentioning

confidence: 99%

“…The work in [ 60 ] used random forests to select features for further use. In other cases, they were used as classifiers [ 40 , 62 , 64 ]. Regression models were also discussed including linear regression [ 38 , 43 , 61 ], support vector regression [ 43 ] and Gaussian process regression [ 43 ].…”

Section: Technical Attributesmentioning

confidence: 99%

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Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

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Section: Categorizationmentioning

confidence: 99%

“…In paper [ 30 , 68 ], they utilized an SVM to analyze position and motion from Kinect data. Researchers also classified gestures using data from wearable devices utilizing an SVM [ 40 ].…”

Section: Technical Attributesmentioning

confidence: 99%

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Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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“…Duncan et al (2019) attached accelerometers on the non-dominant wrist, the dominant wrist, the waist, and the ankle of 30 children and pointed out the advantage of wearing an accelerometer on the ankle to replicate moderate physical activity. In addition to waists, Kang et al (2018) attached IMU sensors to the wrists and the heads of sports participants to recognize gestures in spinning sports. Sarcevic et al (2019) developed a new online classification algorithm, which was applied to wrist-mounted wireless sensors equipped with three triaxial sensors, an accelerometer, a gyroscope, and a magnetometer.…”

Section: Fields Other Than Constructionmentioning

confidence: 99%

Accelerometer-Based Activity Recognition of Workers at Construction Sites

Gondo

Miura

2020

Front. Built Environ.

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Although several methods have been used to capture the motion of workers at construction sites to improve productivity or safety during construction projects, collecting data by image analysis and discriminating between motions are timeconsuming processes. Therefore, it is difficult to use such methods to detect the motion of several workers or over a long time period. The authors applied an accelerometer to capture motion; this apparatus has been used in the health and medical fields. A small, light accelerometer and a simple discrimination program allowed the authors to determine whether a worker was active or inactive based on a threshold of the sum of the signal amplitude areas. Experimental surveys were conducted at construction sites: first, for setting plasterboards for two detached house construction projects and, second, for setting rebar at a large office building site. The findings of this experimental study are as follows. First, an accelerometer can be used in congested construction sites, and data can be obtained continuously. Second, this simple identification of active/inactive workers allows for the measurement of performance and the detection of problems at construction sites. Third, from the data, the hypothesis regarding workers' tendencies can be tested, that is, how workers' skill levels may affect the fluctuation of activity intensity. Therefore, in future studies, by increasing the data on workers, researchers could develop methods to improve the performance of workers.

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“…Limited by the image sensors, only a few postures were modeled. Kang et al [20] and Saha et al [21] applied DT to classify human postures by representative features, such as joint angle and joint distance extracted by body skeleton model. Although the computing cost was not high, the recognition accuracy was relatively low.…”

Section: Introductionmentioning

confidence: 99%

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Zhang

Chen

et al. 2019

Sensors

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Currently, with the satisfaction of people’s material life, sports, like yoga and tai chi, have become essential activities in people’s daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitating from yoga video. They could not know whether they performed standardly without feedback and guidance. In this paper, we proposed a full-body posture modeling and quantitative evaluation method to recognize and evaluate yoga postures to provide guidance to the learner. Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category. The posture data on each body part was regarded as a multidimensional Gaussian variable to build a Bayesian network. The conditional probability of the Gaussian variable corresponding to each body part relative to the Gaussian variable corresponding to the connected body part was used as criterion to quantitatively evaluate the standard degree of body parts. The angular differences between nonstandard parts and the standard model could be calculated to provide guidance with an easily-accepted language, such as “lift up your left arm”, “straighten your right forearm”. To evaluate our method, a wearable device with 11 inertial measurement units (IMUs) fixed onto the body was designed to measure yoga posture data with quaternion format, and the posture database with a total of 211,643 data frames and 1831 posture instances was collected from 11 subjects. Both the posture recognition test and evaluation test were conducted. In the recognition test, 30% data was randomly picked from the database to train BP-ANN and FCM classifiers, and the recognition accuracy of the remaining 70% data was 95.39%, which is highly competitive with previous posture recognition approaches. In the evaluation test, 30% data were picked randomly from subject three, subject four, and subject six, to train the Bayesian network. The probabilities of nonstandard parts were almost all smaller than 0.3, while the probabilities of standard parts were almost all greater than 0.5, and thus the nonstandard parts of body posture could be effectively separated and picked for guidance. We also tested separately the trainers’ yoga posture performance in the condition of without and with guidance provided by our proposed method. The results showed that with guidance, the joint angle errors significantly decreased.

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The gesture recognition technology based on IMU sensor for personal active spinning

Cited by 7 publications

References 2 publications

Digital Twin Coaching for Physical Activities: A Survey

Digital Twin Coaching for Physical Activities: A Survey

Accelerometer-Based Activity Recognition of Workers at Construction Sites

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

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