Quasi-real time estimation of angular kinematics using single-axis accelerometers

Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

Determining anatomical frames via inertial motion capture: A survey of methods

Despite the exponential growth in using inertial measurement units (IMUs) for biomechanical studies, future growth in "inertial motion capture" is stymied by a fundamental challenge - how to estimate the orientation of underlying bony anatomy using skin-mounted IMUs. This challenge is of paramount importance given the need to deduce the orientation of the bony anatomy to estimate joint angles. This paper systematically surveys a large number (N = 112) of studies from 2000 to 2018 that employ four broad categories of methods to address this challenge across a range of body segments and joints. We categorize these methods as: (1) Assumed Alignment methods, (2) Functional Alignment methods, (3) Model Based methods, and (4) Augmented Data methods. Assumed Alignment methods, which are simple and commonly used, require the researcher to visually align the IMU sense axes with the underlying anatomical axes. Functional Alignment methods, also commonly used, relax the need for visual alignment but require the subject to complete prescribed movements. Model Based methods further relax the need for prescribed movements but instead assume a model for the joint. Finally, Augmented Data methods shed all of the above assumptions, but require data from additional sensors. Significantly different estimates of the underlying anatomical axes arise both across and within these categories, and to a degree that renders it difficult, if not impossible, to compare results across studies. Consequently, a significant future need remains for creating and adopting a standard for defining anatomical axes via inertial motion capture to fully realize this technology's potential for biomechanical studies.

Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review

The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings.

Design and Control of a New Biomimetic Transfemoral Knee Prosthesis Using an Echo-Control Scheme

Passive knee prostheses require a significant amount of additional metabolic energy to carry out a gait cycle, therefore affecting the natural human walk performance. Current active knee prostheses are still limited because they do not reply with accuracy of the natural human knee movement, and the time response is relatively large. This paper presents the design and control of a new biomimetic-controlled transfemoral knee prosthesis based on a polycentric-type mechanism. The aim was to develop a knee prosthesis able to provide additional power and to mimic with accuracy of the natural human knee movement using a stable control strategy. The design of the knee mechanism was obtained from the body-guidance kinematics synthesis based on real human walking patterns obtained from computer vision and 3D reconstruction. A biomechanical evaluation of the synthesized prosthesis was then carried out. For the activation and control of the prosthesis, an echo-control strategy was proposed and developed. In this echo-control strategy, the sound side leg is sensed and synchronized with the activation of the knee prosthesis. An experimental prototype was built and evaluated in a test rig. The results revealed that the prosthetic knee is able to mimic the biomechanics of the human knee.

Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty

Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements.

Differential analysis of muscle fatigue induced elbow and wrist tremor in controlled laparoscopic manoeuvring

BACKGROUND

Fatigue induced hand tremor (FIT) is a primary limiting concern for the prolonged surgical intervention in minimally invasive surgery (MIS) and robot-assisted-minimally invasive surgery (RAMIS). A thorough analysis is necessary to understand the FIT characteristics in laparoscopic tool movement. The primary aim of this study is to perform a differential analysis of the elbow and wrist tremor due to muscle fatigue in laparoscopic manoeuvring.

METHODS

We have introduced a joint angle based tremor analysis method, which enables us to perform a differential study of FIT characteristics at the individual joint. Experimental data was acquired from a group of subjects during static and dynamic laparoscopic movement in an imitative RAMIS master manipulation scenario. A repetitive task was performed with a total span of 1 h for observing the effect of muscle fatigue. Along with the joint angle variation, surface electromyography (sEMG) signal was also studied in the analysis.

RESULTS

The wrist tremor is more predominant than tremor generated at the elbow, especially in highly fatigued condition. The high-frequency tremor (>4 Hz) is contributed by the wrist joint. Moreover, the variation of the wrist and elbow tremor ratio was found to be dependent upon the experience of the surgeons.

CONCLUSIONS

In this work, we have investigated the attribution of elbow and wrist joints in FIT during laparoscopic tool manipulation. The outcomes may be useful for the design of robot-assisted surgical manipulator, and can be used for quality assessment of surgical training as well.

Using an inertial navigation algorithm and accelerometer to monitor chest compression depth during cardiopulmonary resuscitation

We present an original method using a low cost accelerometer and a Kalman-filter based algorithm to monitor cardiopulmonary resuscitation chest compressions (CC) depth. A three-axis accelerometer connected to a computer was used during CC. A Kalman filter was used to retrieve speed and position from acceleration data. We first tested the algorithm for its accuracy and stability on surrogate data. The device was implemented for CC performed on a manikin. Different accelerometer locations were tested. We used a classical inertial navigation algorithm to reconstruct CPR depth and frequency. The device was found accurate enough to monitor CPR depth and its stability was checked for half an hour without any drift. Average error on displacement was ±0.5mm. We showed that depth measurement was dependent on the device location on the patient or the rescuer. The accuracy and stability of this small low-cost accelerometer coupled to a Kalman-filter based algorithm to reconstruct CC depth and frequency, was found well adapted and could be easily implemented.

Effectiveness of variable-gain Kalman filter based on angle error calculated from acceleration signals in lower limb angle measurement with inertial sensors

The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors.