Comparison on nonlinear regression algorithms for prediction of skin temperature in lower limb prostheses

Monitoring and predicting the residual limb skin health in amputees is of principal importance as the socket of the prosthesis creates an airtight, warm and damp environment that encourages growth of bacteria and skin breakdown. Elevated stump skin temperatures are one of the major factors that affect the tissue health in that region [1]. Monitoring interface temperature at skin level is notoriously complicated. The problem might be considered notorious because embedding wires and sensors in an elastomer eventually results in elastomer failures because of the high strain induced when donning a liner (amputees roll the liners onto their limbs). Another reason is because placing sensors and wires directly against the skin could cause irritation and chaffing over just a short period of time. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the socket and interface liner materials [2]. This leads to a hypothesis that if the thermal properties of the socket & liner materials are known then the in-socket stump temperature could be accurately predicted by just measuring the prosthetic socket or liner temperature. A mathematical model using the Gaussian processes for machine learning to predict the residual limb skin temperature of the amputee by measuring the in-socket temperature has been developed [3]. Here we compare the performance of Gaussian processes for regression to the other computational method namely support vector machines (SVM).Methods: To investigate the correlation between the position of thermocouples (skin and in-socket), one trans-tibial traumatic amputee was recruited to perform in a 35 minute laboratory protocol (see Table). To monitor and record the skin and in-socket temperatures, four K–type thermocouples via a data logger were used. Two thermocouples were taped onto the residual limb in lateral and medial position. The other two thermocouples were put on the corresponding positions on the liner (in-socket). Activity Time (minutes)Resting/Sitting 10Walk at self-selected pace of 0.62 metres/second on a treadmill10Final rest 15 The temperature profiles of the liner and the residual limb skin were recorded for ambient temperatures of 10°C, and then the same protocol was repeated for 15°C, 20°C, and 25°C. All experiments were conducted in a climate controlled chamber with zero wind velocity and 40% humidity level. It was seen that at any given ambient temperature, the trace of the liner temperature follows that of the residual limb skin. This suggested a possibility to model the liner temperature as a function of skin temperature and create a mathematical model of the same. Different modelling techniques for machine learning were utilized and the results from the Gaussian processes model and support vector regression technique are compared in this study.Results: The results (see Figure) indicate the predictive capability of both Gaussian Processes and SVM modelling techniques at the lateral side at an ambient temperature of 10ºC. The key assumption in Gaussian Process modelling is that our data can be represented as a sample from a multivariate Gaussian distribution. A Gaussian process model infers a joint probability distribution over all possible outputs for all inputs. This form enables the implementation of Bayesian framework where the covariance function is taken in the squared exponential form as in equation (1) Cf=θ1 e^(-〖(xi-xj)^2/(2L^2 ))+σ_n^2 δ_ij (1)where the set of hyperparameters Θ={θ1,L,σ_n} and δ_ij is a delta function whose value is zero for all i ≠ j. After optimizing the hyperparameters, the predictions lie in the 95% confidence interval (±2 standard deviations). This is indicated in (a) in the figure.The SVM modelling technique relies on defining the loss function that ignores errors, which are situated within the certain distance of true value. This epsilon intensive loss function measures the cost of the errors on the training points

Osseointegrated prostheses for rehabilitation following amputation : The pioneering Swedish model.

The direct attachment of osseointegrated (OI) prostheses to the skeleton avoids the inherent problems of socket suspension. It also provides physiological weight bearing, improved range of motion in the proximal joint, as well as osseoperceptive sensory feedback, enabling better control of the artificial limbs by amputees. The present article briefly reviews the pioneering efforts on extremity osseointegration surgeries in Sweden and the development of the OPRA (Osseointegrated Prostheses for the Rehabilitation of Amputees) program. The standard implant design of the OPRA system and surgical techniques are described as well as the special rehabilitation protocols based on surgical sites. The results of long-term follow-up for transradial, transhumeral, and thumb amputee operations are briefly reported including the prospective study of transfemoral amputees according to OPRA protocol. The importance of refinement on implant designs and surgical techniques based on the biomechanical analysis and early clinical trials is emphasized. Future aspects on osseointegration surgery are briefly described, including novel treatment options using implanted electrodes

Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for prediction of skin temperature in lower limb prostheses

Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian Processes for Machine Learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring

A Review of Prosthetic Interface Stress Investigations

Over the last decade, numerous experimental and numerical analyses have been conducted to investigate the stress distribution between the residual limb and prosthetic socket of persons with lower limb amputation. The objectives of these analyses have been to improve our understanding of the residual limb/prosthetic socket system, to evaluate the influence of prosthetic design parameters and alignment variations on the interface stress distribution, and to evaluate prosthetic fit. The purpose of this paper is to summarize these experimental investigations and identify associated limitations. In addition, this paper presents an overview of various computer models used to investigate the residual limb interface, and discusses the differences and potential ramifications of the various modeling formulations. Finally, the potential and future applications of these experimental and numerical analyses in prosthetic design are presented

Towards Natural Control of Artificial Limbs

The use of implantable electrodes has been long thought as the solution for a more natural control of artificial limbs, as these offer access to long-term stable and physiologically appropriate sources of control, as well as the possibility to elicit appropriate sensory feedback via neurostimulation. Although these ideas have been explored since the 1960’s, the lack of a long-term stable human-machine interface has prevented the utilization of even the simplest implanted electrodes in clinically viable limb prostheses.In this thesis, a novel human-machine interface for bidirectional communication between implanted electrodes and the artificial limb was developed and clinically implemented. The long-term stability was achieved via osseointegration, which has been shown to provide stable skeletal attachment. By enhancing this technology as a communication gateway, the longest clinical implementation of prosthetic control sourced by implanted electrodes has been achieved, as well as the first in modern times. The first recipient has used it uninterruptedly in daily and professional activities for over one year. Prosthetic control was found to improve in resolution while requiring less muscular effort, as well as to be resilient to motion artifacts, limb position, and environmental conditions.In order to support this work, the literature was reviewed in search of reliable and safe neuromuscular electrodes that could be immediately used in humans. Additional work was conducted to improve the signal-to-noise ratio and increase the amount of information retrievable from extraneural recordings. Different signal processing and pattern recognition algorithms were investigated and further developed towards real-time and simultaneous prediction of limb movements. These algorithms were used to demonstrate that higher functionality could be restored by intuitive control of distal joints, and that such control remains viable over time when using epimysial electrodes. Lastly, the long-term viability of direct nerve stimulation to produce intuitive sensory feedback was also demonstrated.The possibility to permanently and reliably access implanted electrodes, thus making them viable for prosthetic control, is potentially the main contribution of this work. Furthermore, the opportunity to chronically record and stimulate the neuromuscular system offers new venues for the prediction of complex limb motions and increased understanding of somatosensory perception. Therefore, the technology developed here, combining stable attachment with permanent and reliable human-machine communication, is considered by the author as a critical step towards more functional artificial limbs

Thermal time constant : improving the accuracy of skin temperature predictive modelling in lower limb prostheses

Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health [1]. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. This leads to a hypothesis that if the thermal properties of the socket & liner materials are known then the in-socket skin temperature could be accurately predicted by measuring between the socket and interface liner, rather than at the more technically challenging skin interface

The effect of liner design and materials selection on prosthesis interface heat dissipation

Background and aim: Thermal discomfort often affects prosthesis wearers and could be addressed by increasing liner thermal conductivity. This note explores a liner made from thermally conductive silicone and two additional alternative liner designs. Technique: Thermally conductive silicone was used to create a conductive liner and a hybrid liner. Additionally, one with open elements was made. These were compared with a plain silicone liner and a no liner scenario. Scaled down liner prototypes were used due to the high-cost of the thermally conductive silicone. Temperature decay profiles were collected by attaching thermistors to a heated liner phantom and used to evaluate scenarios. Discussion: No scenario performed much better than the plain silicone liner. Implementation of passive solutions may be easier, but alternative liner materials are unlikely to affect dissipation enough to address thermal discomfort. Based on this work, future research efforts may be better spent developing active thermal discomfort solutions. Clinical relevance Thermal discomfort can increase the probability of skin damage, reduce prosthesis satisfaction and, ultimately, the quality of life. The prosthesis-wearing experience could be improved if thermal discomfort can be addressed by technological improvements

Investigation of Lower-limb Tissue Perfusion during Loading

An extant tissue indentor used for amputee residual limb tissue indentation studies was modified to include laser Doppler flowmetry (LDF) to enable measurement of tissue perfusion during indentation. This device allows quantitative assessment of the mechanical and physiological response of soft tissues to load, as demonstrated by indentation studies of the lower-limb tissues of young healthy subjects. Potential measures of interest include the relative change in tissue perfusion with load and the time delays associated with the perfusion response during tissue loading and unloading. Such measures may prove useful in future studies of residual limb tissues, improving our understanding of tissue viability risk factors for individuals with lower-limb amputation

Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

The term embodiment has become omnipresent within prosthetics research and is often used as a metric of the progress made in prosthetic technologies, as well as a hallmark for user acceptance. However, despite the frequent use of the term, the concept of prosthetic embodiment is often left undefined or described incongruently, sometimes even within the same article. This terminological ambiguity complicates the comparison of studies using embodiment as a metric of success, which in turn hinders the advancement of prosthetics research. To resolve these terminological ambiguities, we systematically reviewed the used definitions of embodiment in the prosthetics literature. We performed a thematic analysis of the definitions and found that embodiment is often conceptualized in either of two frameworks based on body representations or experimental phenomenology. We concluded that treating prosthetic embodiment within an experimental phenomenological framework as the combination of ownership and agency allows for embodiment to be a quantifiable metric for use in translational research. To provide a common reference and guidance on how to best assess ownership and agency, we conducted a second systematic review, analyzing experiments and measures involving ownership and agency. Together, we highlight a pragmatic definition of prosthetic embodiment as the combination of ownership and agency, and in an accompanying article, we provide a perspective on a multi-dimensional framework for prosthetic embodiment. Here, we concluded by providing recommendations on metrics that allow for outcome comparisons between studies, thereby creating a common reference for further discussions within prosthetics research

A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses

A sense of touch is essential for amputees to reintegrate into their social and work life. The design of the next generation of the prostheses will have the ability to effectively convey the tactile information between the amputee and the artificial limbs. This work reviews non-invasive haptic feedback stimulation techniques to convey the tactile information from the prosthetic hand to the amputee’s brain. Various types of actuators that been used to stimulate the patient’s residual limb for different types of artificial prostheses in previous studies have been reviewed in terms of functionality, effectiveness, wearability and comfort. The non-invasive hybrid feedback stimulation system was found to be better in terms of the stimulus identification rate of the haptic prostheses’ users. It can be conclude that integrating hybrid haptic feedback stimulation system with the upper limb prostheses leads to improving its acceptance among users