A Real-Time Wearable Assist System for Upper Extremity Throwing Action Based on Accelerometers

Lian, Kuang‐Yow; Hsu, Wei‐Hsiu; Balram, Deepak; Lee, Chen‐Yi

doi:10.3390/s20051344

Cited by 4 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table 7, similar to the three systems above, the system proposed in this paper uses a small number of sensors to recognize a number of postures. This system has certain advantages in terms of average accuracy compared with the systems proposed in references [18,19]. Although the average accuracy is slightly lower than the system proposed in reference [38], the system proposed in this paper recognizes more postures and achieves good recognition, even for easily confused postures.…”

Section: Foot-acc Xmentioning

confidence: 78%

“…To verify the accuracy and effectiveness of the proposed system, it was compared with references [18,19,38]. Reference [18] established a real-time wearable assist system for upper extremity throwing action based on accelerometers, which used the longest common subsequence (LCS) algorithm to recognize the six phases of baseball throwing 13 Journal of Sensors posture. In [19], an activity assessment chain for evaluating human activity was established using machine learning (ML) to classify six types of indoor rowing stroke postures (one correct and five incorrect).…”

Section: Foot-acc Xmentioning

confidence: 99%

“…Although there are many sports monitoring systems based on IMUs [6,18,19], there is still no system based on deep learning models to recognize a variety of basketball shooting postures. This study proposes a sensor fusion basketball shooting posture recognition system based on a CNN to recognize multiple types of basketball shooting postures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

et al. 2021

View full text Add to dashboard Cite

In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.

show abstract

Section: Foot-acc Xmentioning

confidence: 78%

Section: Foot-acc Xmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Kim and Park (29) developed a golf swing segmentation algorithm from 3-axis acceleration and 3-axis angular velocity data. The algorithm divides the input sequence into five major predefined phases with an average segmentation error of 5-92 ms. Lian et al (28) developed a recognition algorithm for six serial phases of a throwing action in baseball from acceleration data. They achieved a recognition accuracy of 91.42-95.14% for three test subjects for the six phases; however, the estimation error of the segmentation was not evaluated.…”

Section: Motion Timing Estimationmentioning

confidence: 99%

Estimating Timing of Specific Motion in a Gesture Movement with a Wearable Sensor

Murao¹,

Yamada²,

Terada³

et al. 2021

Sensors and Materials

View full text Add to dashboard Cite

Wearable devices with motion sensors, such as accelerometers and gyroscopes, are expected to become popular. There is a lot of research on recognizing gestures using data obtained from motion sensors. A gesture is a one-off motion and its trajectory, i.e., the waveform of the gesture part, is considered to be important. After segmenting the data, gestures are recognized using a template matching method. However, there is no method to accurately detect when a specific action is performed during a gesture. Although it is possible for a player in a game to perform a throwing motion by the user performing a pitching action, it is difficult to reflect a particular moment, such as the user's release point, in the player. The authors previously proposed a method using a wrist-worn sensor for determining the moment of touching a card in competitive karuta (a Japanese card game) and developed a system that judges the player who took a card first in a competitive karuta match. As reported in this paper, we improved the estimation method to apply our study to a variety of gestures other than those in competitive karuta and propose a method of detecting the timing of a specific action. Our system was evaluated for three types of release points, baseball throws, basketball free throws, and dart throws, with 11 subjects who had an accelerometer and a gyroscope attached to the wrist. The percentage of release point estimation errors of 12 ms or less was determined to be 100% for baseball, 87.6% for basketball, and 91.1% for darts.

show abstract

“…An IMU was used to acquire information regarding arm movement of the exoskeleton's user and to use this information as feedback signals in the control loop to improve the response of the exoskeleton. 8,[24][25][26] In Atia and Salah, 24 two IMU sensors were used in a control loop to track arm motion, while in Little et al 25 three IMU sensors were used to measure the elbow flexion and shoulder joint elevation. The measured data were used in their previously developed controller, which was based on gravity compensation.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Abdul-Ameer

2022

Int J Artif Organs

View full text Add to dashboard Cite

This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurement data from IMUs and a goniometer were fed into the suggested Simscape model. The arm torque profiles are key to the control approach and should be traced by torques produced by the exoskeleton actuators to provide comfort and flexibility for the subjects. A DC motor was used as an actuator for the exoskeleton, and its model was used in the physical Simscape model. To reduce the error in the driving torque compared with the reference arm torque, a PID controller was implemented. The results show the potential of our methodology for tracking and controlling the actuator’s torque, in which the mean square error was reduced to less than 0.2 - a significantly low value.

show abstract

A Real-Time Wearable Assist System for Upper Extremity Throwing Action Based on Accelerometers

Cited by 4 publications

References 33 publications

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

Estimating Timing of Specific Motion in a Gesture Movement with a Wearable Sensor

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Contact Info

Product

Resources

About