Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot

Yan, Qingyang; Huang, Jian; Chen, Xiong; Yang, Zhan; Yang, Zhaohui

doi:10.1109/access.2018.2806563

Cited by 19 publications

(14 citation statements)

References 53 publications

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“…This is done via some detecting and monitoring method, which is implemented using sensors to distinguish between the normal and abnormal walking. The experimental results demonstrates that the proposed method in [32] responds better when compared to others, in detection and in recognition but without error correction.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 90%

“…Although the authors mentioned that the proposed method can be used for other systems and faults, it is still not easy to use it in robotics. In [32], the principle component analysis (PCA) is used to detect the faults happened in a robot type Cane during its walking, the walking of this human robot is Vol.25, No.2, December 2020, pp. 164-175 monitored during its healthy case using sensors.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

Faults Diagnosis in Robot Systems: A Review

Alobaidy¹,

Abdul–Jabbar²,

Al-Khayyt³

2020

(AREJ)

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Any robot system consists of mechanical and electrical components. These components are combined and compacted together with a special controller to do a specific work. Mostly and with time obsolescence, faults and errors are happen in robot systems lead to work problems. Many years ago, researchers are working in this field. In this paper, a review study focuses on this field is introduced to make classifications between different types of faults and diagnosis methods those used in the field of robotics. Many types of faults are studied, according to some previous and recent works. The most detected faults can be classified into two parts: hardware and software faults. The hardware faults part is further classified into two main types; the first one is represented in sensors and actuators, while the other is included taking into consideration both position and velocity in joints. These joints which usually connect the links of the arm with each other and ensure contentions to motors, controllers, and many sensors, are really represent the most important side have to be studied. The software faults part includes those errors occurred during programming such as any error happen in signal translation, signal classification or signal identification is considered as a software fault. In this paper most of the fault diagnosis and detection methods of faults types in the field of robotics are studied. According to these studied papers, the hardware fault part is the most important part which must be identified and corrected in real time using smart methods.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 90%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Faults Diagnosis in Robot Systems: A Review

Alobaidy¹,

Abdul–Jabbar²,

Al-Khayyt³

2020

(AREJ)

View full text Add to dashboard Cite

show abstract

“…Due to the different degrees of the upper and lower limb coordination and human-robot coordination, the user has different walking states when using the walking-aid robot [ 29 ]. Therefore, the robot can detect the user’s abnormal walking state by the coordinated motion of the user.…”

Section: Coordinated Motion Fusion-based Walking-aid Robot Systemmentioning

confidence: 99%

A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Huang

Cheng

2018

Sensors

Self Cite

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Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.

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“…Wearable sensors’ perception system usually includes the accelerometer module, gyroscope module, and magnetic module [ 17 ]. Compared with the perception system of HAR based on vision systems such as RGB camera [ 18 ], depth camera [ 19 ], and laser sensor [ 20 ], wearable sensor not only has the advantages of low cost, high efficiency, and easy portability but also avoids the invasion of users’ privacy and the limitation of the vision system in space. Electromyogram (EMG) is increasingly used in wearable devices for activity recognition in recent years [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Wang

et al. 2021

Sensors

Self Cite

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Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

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Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot

Cited by 19 publications

References 53 publications

Faults Diagnosis in Robot Systems: A Review

Faults Diagnosis in Robot Systems: A Review

A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

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