A precise gait phase detection based on high-frequency vibration on lower limbs

Author(s):  
Shuhei Kadoya ◽  
Naohisa Nagaya ◽  
Masashi Konyo ◽  
Satoshi Tadokoro
Author(s):  
Vasileios Syrimpeis ◽  
Vassilis Moulianitis ◽  
Nikos A. Aspragathos ◽  
Elias Panagiotopoulos

Purpose: This paper presents the development of a knowledge based system for the detection of gait phases based on EMGs from muscles of the lower limb. Methods: An empirical analysis of the EMG characteristics for the most representative muscle of every muscle group concerning their suitability for the gait phase detection is presented. The same approach is applied to every lower limb muscle where an EMG could be received. The entities and the decision-making mechanism of the knowledge based system is presented in a formal way. Results: A knowledge based system is built upon the knowledge acquired from this analysis. Finally, an example is presented where the developed knowledge based system is used to support the conceptual design of a drop foot correction system. Conclusions: The knowledge based system can be used in the conceptual design of any rehabilitation system for lower limb disabilities using EMG signals from the lower limbs.


Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5632
Author(s):  
Andrés Trujillo-León ◽  
Arturo de Guzmán-Manzano ◽  
Ramiro Velázquez ◽  
Fernando Vidal-Verdú

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.


Wear ◽  
2021 ◽  
pp. 203814
Author(s):  
Marco Sorgato ◽  
Rachele Bertolini ◽  
Andrea Ghiotti ◽  
Stefania Bruschi

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.


Langmuir ◽  
2013 ◽  
Vol 29 (11) ◽  
pp. 3835-3845 ◽  
Author(s):  
Jeremy Blamey ◽  
Leslie Y. Yeo ◽  
James R. Friend

Electronics ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 78 ◽  
Author(s):  
Nicola Carbonaro ◽  
Federico Lorussi ◽  
Alessandro Tognetti

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