Analysis of gait and coordination for arthroplasty outcome evaluation using body-fixed sensors

The importance of evaluation of an orthopedic operation such as hip or knee arthroplasty has long been recognized. Many definitions of outcome and scoring questionnaires have been used in the past to assess the outcome of joint replacement. However, these assessments are subjective and not accurate enough. In addition, orthopedic surgeons require now more subtle comparisons between potentially efficacious treatments (e.g. two types of prostheses). Therefore, the use of objective instruments that have a better sensitivity and specificity than traditional scoring systems is needed. Gait analysis is one of the most currently used instrumented techniques in this respect. However, a gait analysis system is accessible only in a few specialized laboratories, as it is complex, expensive, need a lot of room space and fixed devices, and not convenient for the patient. In this thesis, we proposed an ambulatory system based on kinematic sensors attached on the lower limbs to overcome the limitations of the previously mentioned techniques. Technically the device is portable, easily mountable, non-invasive, and capable of continuously recording data in long term without hindrance to natural gait. The goal was to provide gait parameters as a new objective method to assess Total Knee Replacement (TKR). New solutions to fusing the data of accelerometers and gyroscopes were proposed to accurately measure lower limbs orientations and joint angles. The methods propose a minimal sensor configuration with one sensor module mounted on each segment. The models consider anatomical aspects and biomechanical constraints. In the proposed techniques, the angles are found without the need for integration, so absolute angles can be obtained which are free from any source of drift. These data were then used to develop a gait analysis system providing spatio-temporal parameters, kinematic curves, and a visualization tool to animate the motion data as synthetic skeletons performing the same actions as the subjects. Moreover, a new algorithm was proposed for assessing and quantification of inter-joint coordination during gait. The coordination model captures the whole dynamics of the lower limbs movements and shows the kinematic synergies at various walking speeds. The model imposes a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion for each individual. It provides a coordination score at various walking speeds which is ranged between 0 and 10. An integration of different analysis tools such as Harmonic Analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. In order to show the effectiveness of the proposed methods in outcome evaluation, we have considered a clinical study where the outcomes of two types of knee prostheses were compared. We conducted a randomized controlled study, including 54 patients, to assess TKR outcome between patients with fixed bearing and mobile bearing tibial plates of implants. The patients were tested preoperatively and postoperatively at 6 weeks, 3 months, 6 months, and 1 year. Various statistical analyses were done to compare the outcomes of the two groups. Finally, we provided objective criteria, using ambulatory gait analysis, for assessing functional recovery following TKR procedure. We showed significant difference between the two groups where the standard clinical evaluation was unable to detect such a difference

An Implantable System for Angle Measurement in Prosthetic Knee

In this work we designed and tested an in-vivo measurement system of prosthetic knee joint angles. The system included a small permanent magnet in the femoral part and three magneto resistance sensors placed in the polyethylene part. The sensor configuration was defined based on sensitivity analysis, signal to noise ratio, saturation of sensors and movements constraints. A mapping algorithm was designed to estimate the orientation of the femoral part in sagittal and coronal plane. For validation the prosthesis was placed in a mechanical simulator equipped with reflective markers tracked by optical motion capture

Real-world gait speed estimation using wrist sensor: A personalized approach

Gait speed is an important parameter to characterize people's daily mobility. For real-world speed measurement, inertial sensors or Global Navigation Satellite System (GNSS) can be used on wrist, possibly integrated in a wristwatch. However, power consumption of GNSS is high and data are only available outdoor. Gait speed estimation using wrist-mounted inertial sensors is generally based on machine learning and suffers from low accuracy due to the inadequacy of using limited training data to build a general speed model that would be accurate for the whole population. To overcome this issue, a personalized model was proposed, which took unique gait style of each subject into account. Cadence and other biomechanically-derived gait features were extracted from wrist-mounted accelerometer and barometer. Gait features were fused with few GNSS data (sporadically sampled during gait) to calibrate the step length model of each subject through online learning. The proposed method was validated on 30 healthy subjects. For walking, it has achieved a median [Interquartile Range] RMSE, bias and precision of 0.05 [0.04-0.06], 0.00 [-0.01 0.00], and 0.06 [0.05 0.07] (m/s), respectively. For running, the errors are 0.14 [0.11 0.17], 0.00 [-0.01 0.02], and 0.18 [0.14 0.23] (m/s), respectively. Results demonstrated that the personalized model provided similar performance as GNSS. It used 50 times less training GNSS data than non-personalized method and achieved even better results. This parsimonious GNSS usage allowed extending battery life. The proposed algorithm met requirements for applications which need accurate, long, real-time, low-power, and indoor/outdoor speed estimation in daily life

A Vibrational Technique for in vitro Intraoperative Prosthesis Fixation Monitoring

Objective: In this paper, a new vibrational modal analysis technique was developed for intraoperative cementless prosthesis fixation evaluation upon hammering. Methods: An artificial bone (Sawbones)-prosthesis system was excited by sweeping of a sine signal over a wide frequency range. The exponential sine sweep technique was implemented to the response signal in order to determine the linear impulse response. Recursive Fourier transform enhancement (RFTE) technique was applied to the linear impulse response signal in order to enhance the frequency spectrum with sharp and distinguishable peak values indicating distinct high natural frequencies of the system (ranging from 15 kHz to 90 kHz). The experiment was repeated with 5 Sawbones-prosthesis samples. Upon successive hammering during the prosthesis insertion, variation of each natural frequency was traced. Results: Compared to classical Fast Fourier Transform, RFTE provided a better tracing and enhancement of frequency components during insertion. Three different types of frequency evolving trends (monotonically increasing, insensitive, and plateau-like) were observed for all samples, as confirmed by a new finite element simulation of the prosthesis dynamic insertion. Two main mechanical phenomena (i.e., geometrical compaction and compressive stress) were shown to govern these trends in opposite ways. Follow-up of the plateau-like trend upon hammering showed that the frequency shift is a good indicator of fixation. Conclusion: Alongside the individual follow-up of frequency shifts, combinatorial frequency analysis provides new objective information on the mechanical stability of Sawbone-prosthesis fixation. Significance: The proposed vibrational technique based on RTFE can provide the surgeon with a new assistive diagnostic technique during the surgery by indicating when the bone-prosthesis fixation is acceptable, and beyond of which further hammering should be done cautiously to avoid bone fracture

Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

Animal‐borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer's high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine‐scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine‐scale behaviour. We address this issue by conceptualising fine‐scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a ‘seek‐and‐learn’ approach: a novel microevent detection step first locates where shock signals occur (‘seek’) by searching for peaks in envelopes of acceleration data. Robust machine learning (‘learn’) employing meaningful features then separates microevents. We showcase the application of our method on tri‐axial accelerometer data collected on 10 free‐living meerkats Suricata suricatta for four fine‐scale foraging behaviours – searching for digging sites, one‐armed digging, two‐armed digging and head jerks during prey ingestion. Annotated videos served as groundtruth, and performance was benchmarked against that of a variety of classical machine learning approaches. Microevent identification (μEvId) with eight features in a three‐node hierarchical classification scheme employing logistic regression at each node achieved a mean overall accuracy of >85% during leave‐one‐individual‐out cross‐validation, and exceeded that of the best classical machine learning approach by 8.6%. μEvId was found to be robust not only to inter‐individual variation but also to large changes in model parameters. Our results show that microevents can be modelled as impulse responses of the animal body‐and‐sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving‐window‐based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition

Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation

Gait bouts (GB), as a prominent indication of physical activity, contain valuable fundamental information closely associated with human & x2019;s health status. Therefore, objective assessment of the GB (e.g. detection, spatio-temporal analysis) during daily life is very important. A feasible and effective way of GB detection in real-world situations is using a wrist-mounted inertial measurement unit. However, the high degree of freedom of the wrist movements during daily-life situations imposes serious challenges for a precise and robust automatic detection. In this study, we deal with such challenges and propose an accurate algorithm to detect GB using a wrist-mounted accelerometer. Features, derived based on biomechanical criteria (intensity, periodicity, posture, and other non-gait dynamicity), along with a Bayes estimator followed by two physically-meaningful post-classification procedures are devised to optimize the performance. The proposed method has been validated against a shank-based reference algorithm on two datasets (29 young and 37 elderly healthy people). The method has achieved a high median [interquartile range] of 90.2 & x005B;80.4, 94.6 & x005D; (& x0025;), 97.2 & x005B;95.8, 98.4 & x005D; (& x0025;), 96.6 & x005B;94.4, 97.8 & x005D; (& x0025;), 80.0 [65.1, 85.9] (& x0025;) and 82.6 & x005B;72.6, 88.5 & x005D; (& x0025;) for the sensitivity, specificity, accuracy, precision, and F1-score of the detection of GB, respectively. Moreover, a high correlation s was observed between the proposed method and the reference for the total duration of GB detected for each subject. The method has been also implemented in real time on a low power consumption prototype

Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors

Abstract—A new method of estimating lower limbs orientations using a combination of accelerometers and gyroscopes is presented. The model is based on estimating the accelerations of ankle and knee joints by placing virtual sensors at the centers of rotation. The proposed technique considers human locomotion and biomechanical constraints, and provides a solution to fusing the data of gyroscopes and accelerometers that yields stable and drift-free estimates of segment orientation. The method was validated by measuring lower limb motions of eight subjects, walking at three different speeds, and comparing the results with a reference motion measurement system. The results are very close to those of the reference system presenting very small errors (Shank: rms = 1 0, Thigh: rms =16) and excellent correlation coefficients (Shank: r = 0 999, Thigh: r = 0 998). Technically, the proposed ambulatory system is portable, easily mountable, and can be used for long term monitoring without hindrance to natural activities. Finally, a gait analysis tool was designed to visualize the motion data as synthetic skeletons performing the same actions as the subjects. Index Terms—Accelerometer and gyroscope, ambulatory system, gait analysis, visualization. I

Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

Animal‐borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer’s high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine‐scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine‐scale behaviour. We address this issue by conceptualising fine‐scale behaviour in terms of characteristic microevents: impulsive movements producing brief (85% during leave‐one‐individual‐out cross‐validation, and exceeded that of the best classical machine learning approach by 8.6%. μEvId was found to be robust not only to inter‐individual variation but also to large changes in model parameters. Our results show that microevents can be modelled as impulse responses of the animal body‐and‐sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving‐window‐based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition