scholarly journals The Turmell-Meter: In Vivo Ankle Kinematics by Using Draw-Wire and Inertial Sensors

2021 ◽  
Author(s):  
Julio Hernando Vargas Riaño
Keyword(s):  
Range Of Motion ◽  
Inertial Sensors ◽  
Screw Theory ◽  
Theory Model ◽  
Battery Management ◽  
Ankle Motion ◽  
Novel Device ◽  
Ankle Kinematics ◽  
Measurement Units

Objective: To implement a prototype specific for human ankle kinematics studies in limited spaces, immobile, or lying down patients. Based on anatomy and anthropometry, using a screw theory model, draw-wire and inertial sensors were employed Methods: We included ankle injury studies to highlight the importance of measuring the in vivo range of motion; we studied the ankle anatomy, biomechanics, and anthropometry to estimate the size and movements of the device. We simulated the biaxial representation of ankle motion through the product of exponential mapping. Finally, we designed a structure based on trilateration by projecting tetrahedrons, an acquisition circuit with firmware and calibration software. Results: The prototype has two main parts: support and adjustable platform. We proposed a method to find the position by projecting three apexes on the base using draw-wire sensors, an acquisition board, a single-board computer, a display, Bluetooth, Wi-Fi, and two inertial measurement units. The power source had battery backup with boost and buck converters. Conclusion: We proposed an ankle model in the screw theory framework, a method for localization, and a novel device for in vivo measurements specific for lying patients on a bed, the ground, outdoors, or remote locations without complex setups. The double-battery management is robust and long lasting. Significance: The device is an alternative for measuring the range of motion in laying down patients. We will use it in modeling, diagnosis, and rehabilitation.<br>

scholarly journals The Turmell-Meter: In Vivo Ankle Kinematics by Using Draw-Wire and Inertial Sensors

2021 ◽  
Author(s):  
Julio Hernando Vargas Riaño
Keyword(s):  
Range Of Motion ◽  
Inertial Sensors ◽  
Screw Theory ◽  
Theory Model ◽  
Battery Management ◽  
Ankle Motion ◽  
Novel Device ◽  
Ankle Kinematics ◽  
Measurement Units

Objective: To implement a prototype specific for human ankle kinematics studies in limited spaces, immobile, or lying down patients. Based on anatomy and anthropometry, using a screw theory model, draw-wire and inertial sensors were employed Methods: We included ankle injury studies to highlight the importance of measuring the in vivo range of motion; we studied the ankle anatomy, biomechanics, and anthropometry to estimate the size and movements of the device. We simulated the biaxial representation of ankle motion through the product of exponential mapping. Finally, we designed a structure based on trilateration by projecting tetrahedrons, an acquisition circuit with firmware and calibration software. Results: The prototype has two main parts: support and adjustable platform. We proposed a method to find the position by projecting three apexes on the base using draw-wire sensors, an acquisition board, a single-board computer, a display, Bluetooth, Wi-Fi, and two inertial measurement units. The power source had battery backup with boost and buck converters. Conclusion: We proposed an ankle model in the screw theory framework, a method for localization, and a novel device for in vivo measurements specific for lying patients on a bed, the ground, outdoors, or remote locations without complex setups. The double-battery management is robust and long lasting. Significance: The device is an alternative for measuring the range of motion in laying down patients. We will use it in modeling, diagnosis, and rehabilitation.<br>

The Turmell-Metre: Using Draw Wire and Inertial Sensors for an In Vivo Ankle Kinematics Study

2021 ◽  
Author(s):  
Julio Hernando Vargas Riaño ◽  
Ángel Valera ◽  
Óscar Agudelo
Keyword(s):  
Inertial Sensors ◽  
Ankle Kinematics ◽  
Ankle Anatomy ◽  
3D Printed

This work presents a device for the ankle kinematics study. It is portable, affordable and some parts can be 3D printed. It is based on the ankle anatomy and biomechanics, also, it can be applied for the characterization of the ankle axis or another models of the ankle. <br>

scholarly journals The Turmell-Metre: Using Draw Wire and Inertial Sensors for an In Vivo Ankle Kinematics Study

2021 ◽  
Author(s):  
Julio Hernando Vargas Riaño ◽  
Ángel Valera ◽  
Óscar Agudelo
Keyword(s):  
Inertial Sensors ◽  
Ankle Kinematics ◽  
Ankle Anatomy ◽  
3D Printed

This work presents a device for the ankle kinematics study. It is portable, affordable and some parts can be 3D printed. It is based on the ankle anatomy and biomechanics, also, it can be applied for the characterization of the ankle axis or another models of the ankle. <br>

scholarly journals Influence of surgery involving tendons around the knee joint on ankle motion during gait in patients with cerebral palsy

2017 ◽  
Vol 2 (3) ◽  
pp. 2473011417S0002
Author(s):  
Seung Yeol Lee ◽  
Kyoung min Lee ◽  
Soon-Sun Kwon ◽  
Sangho Chun
Keyword(s):  
Cerebral Palsy ◽  
Range Of Motion ◽  
Dynamic Range ◽  
Rectus Femoris ◽  
Ankle Dorsiflexion ◽  
Knee Surgery ◽  
Initial Contact ◽  
Ankle Motion ◽  
Ankle Kinematics ◽  
Distal Hamstring Lengthening

Category: Ankle Introduction/Purpose: The gastrocnemius, a biarticular muscle that crosses the knee and ankle, acts as a knee flexor as well as an ankle plantar flexor. Although simultaneous motion of the knee and ankle joints is required for many activities including standing, running, swimming, and cycling, the change in ankle motion during gait has not been described in patients with cerebral palsy who underwent distal hamstring lengthening or distal hamstring lengthening with rectus femoris transfer. Therefore, we aimed to evaluate the influence of surgery involving tendons around the knee on ankle motion during gait in cerebral palsy patients. Methods: The analysis included data regarding 55 limbs from 34 patients with spastic cerebral palsy, who were followed-up after they had undergone distal hamstring lengthening with or without additional rectus femoris transfer. Mean age of the patients at time of the knee surgery was 11.2 ± 4.7 years. Preoperative and postoperative kinematic variables that were extracted from three-dimensional gait analyses were compared to assess the change in ankle motion after surgery involving tendons around the knee. The postoperative 3D gait analysis was performed at a mean of 0.9 ± 1.3 years after the surgery. The outcome measures were relevant kinematics parameters including peak ankle dorsiflexion at initial contact, peak ankle dorsiflexion during stance, ankle peak dorsiflexion during swing, and dynamic range of motion of the ankle. A linear mixed model was constructed to estimate the changes in ankle motion after adjusting for multiple factors. Results: We estimated that peak ankle dorsiflexion at initial contact, peak ankle dorsiflexion during stance, ankle peak dorsiflexion during swing, and dynamic range of motion of the ankle decreased, respectively, by 0.4º (p=0.016), 0.6º (p<0.001), 0.2º (p=0.038), and 0.5º (p=0.006) per degree increase in total range of motion of the knee after knee surgery (Table). Estimated ankle peak dorsiflexion in the swing phase increased by 0.4º per degree increased in postoperative peak knee flexion in the swing phase (Table). Age at the time of the knee surgery did not significantly affect ankle kinematics. Conclusion: Improvement in total knee range of motion was correlated with a decrease in ankle kinematics after surgery involving tendons around the knee. Knee surgery may reduce the need for an additional surgical procedure involving the ankle joint. Because the simultaneous motion of the knee and ankle joints is cross-linked, surgeons should be aware of potential changes in the ankle joint after knee surgery.

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

Hand ◽  
2021 ◽  
pp. 155894472110146
Author(s):  
Francisco R. Avila ◽  
Rickey E. Carter ◽  
Christopher J. McLeod ◽  
Charles J. Bruce ◽  
Davide Giardi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Range Of Motion ◽  
Health Care Professionals ◽  
Inertial Sensors ◽  
Measurement Techniques ◽  
Infrared Camera ◽  
Wearable Devices ◽  
Sensor Technology ◽  
Accuracy Evaluation ◽  
Wearable Sensor

Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

Sagittal balance influences range of motion: an in vivo study with the ProDisc-C

2009 ◽  
Vol 9 (2) ◽  
pp. 128-133 ◽  
Author(s):  
Doron Rabin ◽  
Rudolf Bertagnoli ◽  
Nicholas Wharton ◽  
Gwynedd E. Pickett ◽  
Neil Duggal
Keyword(s):  
Range Of Motion ◽  
Sagittal Balance ◽  
In Vivo Study

Abnormalities Detection of IMU Based on PCA in Motion Monitoring

2012 ◽  
Vol 224 ◽  
pp. 533-538 ◽  
Author(s):  
Jing Zhou ◽  
Steven Su ◽  
Ai Huang Guo ◽  
Wei Dong Chen
Keyword(s):  
Measurement Method ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Principal Component ◽  
Body Sensor Networks ◽  
Remote Measurement ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Multiple Degrees Of Freedom ◽  
Measurement Units

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people’s daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling’s T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

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