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Studies have shown that the use of cryotherapy after a total knee arthroplasty can have beneficial effect on blood loss, pain and medication usage. In this study, the effect of the applied cryotherapy procedure is investigated. 52 patients underwent a total knee arthroplasty. The test group received continuous cooling, whereas the control group received manual conventional cooling with ice dressing. The knee circumference and range of motion, medication use, satisfaction and pain were investigated. There is no statistical significant difference in pain and medication usage. A significant difference is observed in the swelling of the knee on the first postoperative day, the range of motion on the 7 th , 10 th , 11 th and 12 th postoperative day, and the satisfaction rate. This study shows that continuous cooling has a positive effect on the swelling and range of motion of the knee, and on the satisfaction of the treatment. Clinical trial registration number : Clinical trial number : EudraCT2015-000259-26.

BACKGROUND AND AIM: Foot orthoses alter the kinematics and kinetics in gait. With the increasing importance of evidence based practice and with the permanent development of subtractive manufacturing and introduction of additive manufacturing, there is a growing need for quantification of orthoses parameters. We describe a measurement method and protocol to quantify different parameters of a foot orthosis. TECHNIQUE: A texture analyser is used to impose a displacement of the surface of the orthosis, while the applied force is measured. The measured points are determined based on the location of anatomical landmarks on the foot. Out of the measured data, parameters are calculated representing the stiffness, compression set and shape. DISCUSSION: To illustrate the proposed technique, five different parameters are extracted from three example orthoses. Results show the added value of the proposed technique as the parameters are not only defined by the material but also by the shape. ispartof: pages:5348-5351 ispartof: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings vol:2019 pages:5348-5351 ispartof: IEEE 41st International Engineering in Medicine and Biology Conference location:Berlin, Germany date:23 Jul - 27 Jul 2019 status: published

People with a transtibial amputation worldwide rely on their prosthetic socket to regain their mobility. Patient comfort is largely affected by the weight and strength of these prosthetic sockets. The use of additive manufacturing could give the prosthetist a range of new design possibilities when designing a prosthetic socket. These new design possibilities can in turn lead to improved socket designs and more comfortable prosthetic sockets. This new way of designing and producing prosthetic sockets radically differs from the manual traditional production process. This makes it difficult for prosthetists to understand how all these new design possibilities influence the mechanical properties of the additive manufactured prosthetic socket. Therefore there is a growing need for a method to evaluate the strength and stiffness of newly developed socket designs.We propose a method to evaluate the strength and stiffness of prosthetic sockets. A robotic gait simulator is used to apply realistic kinetics of amputee gait to the tested socket. A Digital Image Correlation (DIC) system is then used to measure the deformation of a prosthetic socket under different loading conditions. This way it is possible to check if plastic deformation will occur in the designed transtibial socket. Furthermore it is possible to assess the effect of cyclic loading on the 3D printed socket.To illustrate the proposed method, a transtibial prosthetic socket was designed using CAD software and produced with laser sintering PA12. DIC measurements were performed on this transtibial socket both before and after it was subjected to a cyclic load of 1 million cycles (mimicking realistic amputee gait). ispartof: pages:5382-5385 ispartof: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings vol:2019 pages:5382-5385 ispartof: International Engineering in Medicine and Biology Conference (ieee embc) location:Berlin date:23 Jul - 27 Jul 2019 status: published

People with physical disabilities and people with moderate, severe to profound intellectual disabilities often need aids for activities of daily living (AADLs). Currently almost all of them are provided with prefabricated aids which often do not fit well or insufficiently. A bespoke aid would be an added-value here. In this paper, approaches to design such bespoke AADLs with different technical complexities are elaborated. As an example the implementation and workflow of two cases are covered: a bespoke joystick handle for an electric wheelchair and a plate-raiser. For both cases, an overview of the practical implementation is given, including the technical considerations. Also an implementation is provided using a 3D scan of the end-user or object on which the AADL is fitted, and one that is usable without 3D scan. The choice of implementation strategy is based on the needs of the end-user, and the preferences of the health care professional who designs the aid. By providing different options for health care professionals to design (and produce) bespoke AADLs, this study aims to lower the threshold to provide end-users with bespoke AADLs that are better tailored to their needs, more specifically when the bespoke AADLs would benefit them better than the prefabricated ones. Hereby, this study aims to contribute to the quality of life and participation of the end-users.

Purpose Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. Method We performed a thorough literature review and had contact with manufacturers of commercially available devices. Results This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. Conclusion Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user⿿s seizure types and personal preferences.

PURPOSE: The aim of our study was to test the efficacy of the VARIA system (video, accelerometry, and radar-induced activity recording) and validation of accelerometry-based detection algorithms for nocturnal tonic-clonic and clonic seizures developed by our team. METHODS: We present the results of two patients with tonic-clonic and clonic seizures, measured for about one month in a home environment with four wireless accelerometers (ACM) attached to wrists and ankles. The algorithms were developed using wired ACM data synchronized with the gold standard video-/electroencephalography (EEG) and then run offline on the wireless ACM signals. Detection of seizures was compared with semicontinuous monitoring by professional caregivers (keeping an eye on multiple patients). RESULTS: The best result for the two patients was obtained with the semipatient-specific algorithm which was developed using all patients with tonic-clonic and clonic seizures in our database with wired ACM. It gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night. This included 13 extra seizures detected (31%) compared with professional caregivers' observations. CONCLUSION: While the algorithms were previously validated in a controlled video/EEG monitoring unit with wired sensors, we now show the first results of long-term, wireless testing in a home environment. publisher: Elsevier articletitle: Long-term accelerometry-triggered video monitoring and detection of tonic–clonic and clonic seizures in a home environment: Pilot study journaltitle: Epilepsy & Behavior Case Reports articlelink: http://dx.doi.org/10.1016/j.ebcr.2016.03.005 content_type: article copyright: © 2016 The Authors. Published by Elsevier Inc. ispartof: Epilepsy & Behavior Case Reports vol:5 pages:66-71 ispartof: location:United States status: published

Purpose: There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes are attached to the scalp. EEG analysis is also labour-intensive and has yet to be automated and adapted for real-time monitoring. It is therefore usually performed in a hospital setting, for a few days at the most. The goal of this article is to provide an overview of body signals that can be measured, along with corresponding methods, state-of-art research, and commercially available systems, as well as to stress the importance of a good detection system. Method: Narrative literature review. Results: A range of body signals can be monitored for the purpose of seizure detection. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP, and of movement, as many seizures have a motor component. Conclusion: The most effective seizure detection systems are multimodal. Such systems should also be comfortable and low-power. The body signals and modalities on which a system is based should take account of the user's seizure types and personal preferences. (C) 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications.We performed a thorough literature review and had contact with manufacturers of commercially available devices.This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component.Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

The registration of plantar pressure images is a widely used technique to support human gait analysis. In plantar pressure images, most of the time conventionally derived features are used for further processing. Recently, automatic feature extraction based on PCA and kPCA is being used, to increase the information that can be extracted from this data. In this paper, we describe our work flow and a case study on the application of predicting two pressure features and a non-pressure feature out of the automatically derived PCA features. This includes the normalization of the pressure images, the PCA based feature extraction, and building and testing the regression model based on a linear and kernel SVM.

Multi-segmental foot kinematics have been analysed by means of optical marker-sets or by means of inertial sensors, but never by markerless dynamic 3D scanning (D3DScanning). The use of D3DScans implies a radically different approach for the construction of the multi-segment foot model: the foot anatomy is identified via the surface shape instead of distinct landmark points. We propose a 4-segment foot model consisting of the shank (Sha), calcaneus (Cal), metatarsus (Met) and hallux (Hal). These segments are manually selected on a static scan. To track the segments in the dynamic scan, the segments of the static scan are matched on each frame of the dynamic scan using the iterative closest point (ICP) fitting algorithm. Joint rotations are calculated between Sha-Cal, Cal-Met, and Met-Hal. Due to the lower quality scans at heel strike and toe off, the first and last 10% of the stance phase is excluded. The application of the method to 5 healthy subjects, 6 trials each, shows a good repeatability (intra-subject standard deviations between 1° and 2.5°) for Sha-Cal and Cal-Met joints, and inferior results for the Met-Hal joint (>3°). The repeatability seems to be subject-dependent. For the validation, a qualitative comparison with joint kinematics from a corresponding established marker-based multi-segment foot model is made. This shows very consistent patterns of rotation. The ease of subject preparation and also the effective and easy to interpret visual output, make the present technique very attractive for functional analysis of the foot, enhancing usability in clinical practice. publisher: Elsevier articletitle: Dynamic 3D scanning as a markerless method to calculate multi-segment foot kinematics during stance phase: Methodology and first application journaltitle: Journal of Biomechanics articlelink: http://dx.doi.org/10.1016/j.jbiomech.2014.06.010 content_type: article copyright: Copyright © 2014 The Authors. Published by Elsevier Ltd. ispartof: Journal of Biomechanics vol:47 issue:11 pages:2531-2539 ispartof: location:United States status: published

Objective Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. Methods Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. Results When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. Conclusions A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties. publisher: Elsevier articletitle: Detecting rare events using extreme value statistics applied to epileptic convulsions in children journaltitle: Artificial Intelligence in Medicine articlelink: http://dx.doi.org/10.1016/j.artmed.2013.11.007 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved. ispartof: Artificial Intelligence in Medicine vol:60 issue:2 pages:89-96 ispartof: location:Netherlands status: published

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency and wavelet based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and non-epileptic movement. This classification is only based on a non-parametric estimate of the probability density function of normal movements. Such approach allows to build patientspecific models to classify movement data without the need for seizure data that is rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure, otherwise it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a Positive Predictive Value (PPV) of 60.04%. However, there is a noticeable inter-patient difference. ispartof: IEEE Journal of Biomedical and Health Informatics vol:18 issue:3 pages:1026-1033 ispartof: location:United States status: published

The assessment of feet by foot experts depends on their background knowledge and preferences. To streamline foot assessments, we want to pinpoint measureable values to each foot characteristic.In t...

Introduction The clinical assessment of feet is mostly based on the experience of foot experts, and varies with their clinical background. To assess feet, experts typically use different techniques and equipment, such as podoscopes, blueprints, pressure plates, or goniometers. To reach a better consistency among experts, it is necessary to compare their results to accurate, quantitative measurements. Purpose of the study This study aims to identify which clinical feet characteristics can be measured robustly visually, and which ones benefit from adding specific equipment in the measurement procedure. As a first step, we want to identify which foot characteristics can be accurately predicted from the quantitative data from different measurement set-ups. ispartof: Footwear Science vol:7 pages:S3-S4 ispartof: location:Liverpool status: published

We developed a knee brace to measure the knee angle and implicitly the flexion/extension (f/e) of the knee joint during daily activities. The goal of this study is to classify and validate a limited set of physical activities on ten young healthy subjects based on knee f/e. Physical activities included in this study are walking, ascending and descending of stairs, and fast locomotion (such as jogging, running, and sprinting) at self-selected speeds. The knee brace includes 2 accelerometers for static measurements and calibration and an inductive sensor for dynamic measurements.Aswe focus on physical activities, the inductive sensorwill provide the required information on knee f/e. In this study, the subjects traversed a predefined track which consisted of indoor paths, outdoor paths, and obstacles. The activity classification algorithm based on peak detection in the knee f/e angle resulted in a detection rate of 95.9% for walking, 90.3% for ascending stairs, 78.3% for descending stairs, and 82.2% for fast locomotion.We conclude that we developed a measurement device which allows long-termand ambulatorymonitoring. Furthermore, it is possible to predict the aforementioned activities with an acceptable performance. ispartof: ISRN Biomedical Engineering vol:2013 pages:1-10 status: published

Epileptic seizure detection is traditionally done using video/electroencephalogram (EEG) monitoring, which is not applicable in a home situation. In recent years, attempts have been made to detect the seizures using other modalities. In this paper we investigate if a combined usage of accelerometers attached to the limbs and video data would increase the performance compared to a single modality approach. Therefore, we used two existing approaches for seizure detection in accelerometers and video and combined them using a linear discriminant analysis (LDA) classifier. The results for a combined detection have a better positive predictive value (PPV) of 95.00% compared to the single modality detection and reached a sensitivity of 83.33%.

Long-term home monitoring of epileptic seizures is not feasible with the gold standard of video/electro-encephalography (EEG) monitoring. The authors developed a system and algorithm for nocturnal hypermotor seizure detection in pediatric patients based on an accelerometer (ACM) attached to extremities. Seizure detection is done using normal movement data, meaning that the system can be installed in a new patient's room immediately as prior knowledge on the patient's seizures is not needed for the patient-specific model. In this study, the authors compared video/EEG-based seizure detection with ACM data in seven patients and found a sensitivity of 95.71% and a positive predictive value of 57.84%. The authors focused on hypermotor seizures given the availability of this seizure type in the data, the typical occurrence of these seizures during sleep, i.e., when the measurements were done, and the importance of detection of hypermotor seizures given their often refractory nature and the possible serious consequences. To our knowledge, it is the first detection system focusing on this type of seizure in pediatric patients. (C) 2012 Elsevier Inc. All rights reserved.

Monitoring of epileptic patients is usually done by video/EEG-monitoring which is considered as the golden standard. Due to some disadvantages of this method, this method is not feasible to use in long term home monitoring. Video monitoring provides a solution to this problem as it can monitor the patient in a non-contacting way. An algorithm is developed to detect movement epochs in nocturnal datasets for pediatric epileptic patients. The performance was measured using a threefold crossvaildation, which resulted in a sensitivity of 1 and a positive predictive value above 0.85.

The monitoring of epileptic seizures is mainly done by means of video/EEG-monitoring. Although this method is considered as the golden standard, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient’s movement. This makes long term home monitoring not feasible. A detection system with accelerometers attached to the wrists and ankles can solve this problem. Nocturnal frontal lobe seizures often include bicycle pedaling movements or uncontrolled movements with the arms which are clearly visible in the accelerometer signals. Data from three patients suffering from nocturnal frontal lobe seizures is used in this paper for the development of an automatic detection algorithm for this type of seizure. First movement epochs are detected as a preprocessing step by calculating the standard deviation of a sliding window. Afterwards a moving average filter is applied to the data and thresholds are set to the signals of the arms and legs to detect the seizures. This resulted in an algorithm with a sensitivity of 91.67% and a specificity of 83.92%.

Introduction The detection and analysis of epileptic seizures is typically done by video-electroencephalogram monitoring. Although it is considered as the Golden Standard, it has disadvantages: the electroencephalogram electrodes are uncomfortable to wear for a longer period of time and hospitalization is often required. The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy.

The foot is a complex structure, bearing the responsibility for the load and propulsion of the entire human body during walking. Important variations in foot form and structure exist individually, ...

Until now foot motion is mostly studied by markerbased 3D analysis. However, a recently developed dynamic 3D scanner is able to scan the entire foot surface at 49 Hz (Vialux). This creates a variety of possibilities for analysis of foot motion during gait. The aim of this study is the extraction of a segmented foot model from a dynamic 3D scan. Such a foot model would enable a motion analysis without the need to predefine segments, with data from quick and non-invasive measurements.

In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition [8].We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.

Treatment or prevention of specific foot problems often requires an analysis of the biomechanics of the foot. These analyses can be performed by different experts. Specifically, in Flanders, they may be performed by medical doctors in orthopaedics and rehabilitation, orthopaedic technologists, or podiatrists. It is well known that there is no standardization yet of clinical methods to analyse foot biomechanics [1,2]. The purpose of this study was to investigate to what extent foot experts differ in biomechanical foot analyses. The presented data is a pilot study on 6 subjects, analysed by 9 experts. The complete study will be performed on 78 subjects by 10 experts. In that larger study, all subjects will also be analysed with advanced gait analyses methods. This to correlate the clinical data to objective, quantitative data, and develop foot typology.