5th Dutch Bio-Medical Engineering Conference 2015
22-23 January 2015, Egmond aan Zee, The Netherlands
13:30   Musculoskeletal Mechanics
15 mins
Octavio Martinez Manzanera, Natasha Maurits
Abstract: Gait is the pattern of movement of the limbs during locomotion and can be affected by different movement disorders. A neurological symptom that affects gait is ataxia, which is a lack of coordination of movements. There are three types of ataxia: sensory, cerebellar and vestibular ataxia. To achieve normal gait many brain areas have to coordinate their activity. Damage to one of these areas can produce specific abnormalities in the walking pattern. In cerebellar ataxia, for example, a lesion in the cerebellum is thought to be the cause of ataxia1. To evaluate a movement disorder in the clinic, rating scales are typically used. For ataxia, the Scale for the Assessment and Rating of Ataxia (SARA) is used. However, this rating scale has limitations due to the subjective interpretation of the scale and due to the experience and knowledge required from the evaluators. Objective measurements of the kinematics of gait can help in the diagnosis of ataxia by overcoming some disadvantages of the rating scales. The kinematics of gait have been analyzed extensively in healthy subjects with different techniques (chronophotography, video recordings, 3D motion capture using passive or active markers and inertial systems with gyroscopes, accelerometers and magnetic sensors). While there are multiple studies of gait in patients with movement disorders the quantitative analysis of gait in ataxic patients has so far been limited. In this study we included three groups of five age-matched patients with “indisputable ataxia”, “mixed ataxia” and developmental coordination disorder (DCD) which is an impairment in the organization of motor skills that also might affect gait. The aim of this study is to investigate in a pilot setting whether quantitative analysis of gait using inertial sensors may be used to distinguish between these three groups. Gait from each patient was recorded using a ten degrees of freedom (DoF) orientation sensor (composed of accelerometers, gyroscopes, magnetometers and altitude sensors). This sensor allowed to derive spatial and temporal features that were used to compare the different groups. The results were compared against the clinical evaluation of clinicians. The ten DoF sensor has the advantage that it allows to combine the signals in a sensor fusion algorithm2 to obtain a spatial representation of the walking pattern. Our hypothesis is that this representation can be related better to the qualitative observation of the clinician than commonly used spatial and temporal features. REFERENCES [1] M. Manto and D. Marmolino, “Cerebellar ataxias”, Curr Opin Neurol., Vol. 255, pp. 1244-12, (2009). [2] S. Madgwick et al., “Estimation of IMU and MARG orientation using a gradient descent algorithm”, IEEE International Conference on Rehabilitation Robotics. 2011.
15 mins
Kim van Schooten, Mirjam Pijnappels, Sietse Rispens, Petra Elders, Paul Lips, Jaap van Dieën
Abstract: Introduction Ambulatory measurements of trunk accelerations can provide valuable information on the amount and quality of daily-life activities and contribute to the identification of individuals at risk of falls. We investigated predictive value for falls of parameters describing the amount and quality of gait. We further determined to what extent fall prediction models based on commonly used questionnaires and tests could be improved by incorporating these gait characteristics obtained from accelerometry during daily life. Methods One week of trunk accelerometry (DynaPort MoveMonitor) was obtained in 169 older adults (mean age 75, SD 7). In addition, validated questionnaires on fall-risk factors, grip strength and trail making test were obtained. Based on the accelerometer data, five types of activities were classified: locomotion, sitting, standing, lying and an unclassified category. The total duration of these activities was calculated and for the locomotion bouts we additionally estimated characteristics describing gait quality i.e. walking speed, stride frequency, intensity, variability, symmetry, smoothness and local stability. Participants were classified as fallers if they experienced one or more falls during 6 months prospective follow up as obtained by daily fall diaries and monthly telephone contact. The association between potential predictors and prospective falls was investigated using logistic regression and multivariate logistic regression was used for the fall prediction models. We are currently analysing data of an additional 140 older adults and expect to present these results during the symposium. Results Of all participants, 35% experienced at least one fall during six-months follow-up. The risk of falls was significantly higher for individuals with a lower walking speed, lower stride frequency, lower intensity of gait, less symmetry, less smoothness and lower local stability of gait. Predictive ability based on questionnaires, grip strength and trail making test (area under the curve 0.68) improved substantially by accelerometry-derived parameters of the amount of gait (number of strides), gait quality (local dynamic stability, intensity, smoothness and entropy) and their interactions (area under the curve 0.82). Using these variables, 72.2% of the non-fallers and 81.6% of the fallers were correctly classified. Conclusions Daily-life accelerometry contributes substantially to the identification of individuals at risk of falls, and can predict falls with good accuracy. Parts of these results have already been presented at ICCSS&HA Groningen 2014.
15 mins
Hans Essers, Alessio Murgia, Arjen Bergsma, Paul Verstegen, Kenneth Meijer
Abstract: Muscular dystrophies (MDs) are characterized by progressive muscle wasting and weakness. Several studies have been conducted to investigate the influence of arm supports in an attempt to restore arm function. Lowering the load allows the user to employ the residual muscle force for movement as well as for posture stabilization. In this pilot study three conditions were investigated during a reaching task performed by three healthy subjects and three MD subjects: a control condition involving reaching; a similar movement with gravity compensation using braces to support the forearm; an identical reaching movement in simulated zero-gravity. In the control condition the highest values of shoulder moments were present, with a maximum of about 6 Nm for shoulder flexion and abduction. In the gravity compensation and zero gravity conditions the maximum shoulder moments were decreased by more than 70% and instead of increasing during reaching, they remained almost unvaried, fluctuating around an offset value less than 1 Nm. Similarly, the elbow moments in the control condition were the highest with a peak around 3.3 Nm for elbow flexion, while the moments were substantially reduced in the remaining two conditions, fluctuating around offset values between 0 to 0.5 Nm. In conclusion, gravity compensation by lower arm support is effective in healthy subjects and MD subjects and lowers the amount of shoulder and elbow moments by an amount comparable to a zero gravity environment. However the influence of gravity compensation still needs to be investigated on more people with MDs in order to quantify any beneficial effect on this population.
15 mins
Riza Bayoglu, Jasper Homminga, Bart Koopman
Abstract: Interest in musculoskeletal models of the human spine has increased over the last decades to investigate clinical problems such as low back pain and spinal metastasis. Understanding the physiological state of loading in the spine via such models will guide surgeons to plan optimal treatment methods, and to develop diagnostic tools for patients. These models are advantageous in the sense that they can provide essential information about the loads in the vertebral column and muscle forces in the trunk, which is not possible to measure in vivo. Recently, a detailed musculoskeletal model of the thoracolumbar spine with articulated ribs was introduced in AnyBody [1] by a group from ETH Zürich. Multi-axial stiffnesses of all lumbar and thoracic intervertebral discs (IVDs) were defined to account for the effects of passive tissues, as well as elastic elements to model the lumbar ligaments, costo-vertebral articulations, costal cartilages and costo-sternal joints. In this study, we compared the results obtained from this model against in vivo data [2,4] to gain insights into future improvements needed in the model. Five body postures were analysed: standing, extension (15° and 20°), and flexion (30° and 36°). Compressive forces in the IVDs were used to estimate intradiscal pressures by using disc cross sectional areas from literature [3]. Disc pressures for extension and flexion movements were normalized to values in standing, and were compared at middle (T6T7, T7T8), lower thoracic (T9T10, T10T11) and lumbar (L4L5) spine levels. The normalized pressure difference between the model and the in vivo measurements in the mid-region was 56% for extension (15°) and 102% for flexion (30°). In the lower region, they were 55% and 65%, respectively. At L4L5 level, differences of 81% for extension (20°) and 33% for flexion (36°) were found when compared with another in vivo study [4]. Based on this in vivo data, the thoracolumbar spine model needs improvement in order to estimate the joint reaction forces in the IVDs. Analyses for lateral bending and axial rotation motions will follow soon. REFERENCES [1] D. Ignasiak, et al., “Detailed musculoskeletal model of thoracolumbar spine with articulated rib cage”, Poster session presented at 7th World congress of biomechanics, Boston Massachusetts, July 6-11, (2014). [2] D. J. Polga et al., “Measurement of in vivo intradiscal pressure in healthy thoracic intervertebral discs”, Spine, Vol. 29, pp. 1320-1324, (2004). [3] M. M. Panjabi et al., “Thoracic human vertebrae. Quantitative three-dimensional anatomy”, Spine, Vol. 16, pp. 888-901, (1991). [4] H. J. Wilke et al., “Intradiscal pressure together with anthropometric data – a data set for the validation of models”, Clinical Biomechanics, Vol. 16, pp. S111-S126, (2001).
15 mins
Marco Marra, Valentine Vanheule, René Fluit, Bart Koopman, John Rasmussen, Nico Verdonschot, Michael Andersen
Abstract: Musculoskeletal (MS) models are useful to gain information on in vivo biomechanics that would be otherwise very difficult to obtain. However, before entering the clinical routine MS models must be thoroughly validated. This study presents a novel MS modelling framework capable of integrating the patient-specific MS architecture in a very detailed way, and simultaneously simulating body level dynamics and secondary knee kinematics. The model predictions were further validated against publicly available in vivo experimental data. The bone geometries were segmented from CT images of a patient with an instrumented Total Knee Arthroplasty (TKA) from the “Grand Challenge Competition to Predict In Vivo Knee Loads” dataset. These were inputted into an advanced morphing technique in order to scale the MS architecture of the new TLEM 2.0 model to the specific patient. A detailed 11-DOF model of the knee joint was constructed that included ligaments and rigid contact. An inverse kinematic and a force-dependent kinematic technique were utilized to simulate one gait cycle and one right-turn trial. Tibiofemoral (TF) joint contact force predictions were evaluated against experimental TF forces recorded by the TKA prosthesis, and secondary knee kinematics against experimental fluoroscopy data. The coefficient of determination and the root-mean-square error between predicted and experimental tibiofemoral forces were larger than 0.9 and smaller than 0.3 body-weights, respectively, for both gait and right-turn trials. Secondary knee kinematics were estimated with an average Sprague and Geers’ combined error as small as 0.06. The modelling strategy proposed permits a high level of patient-specific personalization and does not require any non-physiological parameter tuning. The very good agreement between predictions and experimental in vivo data is promising for the future introduction of the model into clinical applications.
15 mins
Dirk Weenk, Bert-Jan van Beijnum, Salma Goaied, Chris Baten, Hermie Hermens, Peter Veltink
Abstract: Introduction and past research: In previous work we presented an algorithm for automatically identifying the body segment to which an inertial sensor is attached during walking [1]. Using this method, the set-up of inertial motion capture systems becomes easier and attachment errors are avoided. The user can place (wireless) inertial sensors on arbitrary body segments. Then, after walking for a few steps, the segment to which each sensor is attached is identified automatically. To classify the sensors, a decision tree was trained using ranked features extracted from magnitudes, x- y- and z-components of accelerations, angular velocities and angular accelerations. Method: Drawback of using ranking and correlation coefficients as features is that information from different sensors needs to be combined. Therefore we started looking into a new method using the same data and the same extracted features as in [1], but without using the ranking and the correlation coefficients between different sensors. Instead of a decision tree, we used logistic regression for classifying the sensors [2]. Unlike decision trees, with logistic regression a probability is calculated for each body part on which the sensor can be placed. To develop a method that works for different activities of daily living, we recorded 18 activities of ten healthy subjects using 17 inertial sensors. Walking at different speeds, sit to stand, lying down, grasping objects, jumping, walking stairs and cycling were recorded. The goal is – based on the data of single sensor — to predict the body segment to which this sensor is attached, for different activities of daily living. Results: A logistic regression classifier was developed and tested with 10-fold crossvalidation using 31 walking trials of ten healthy subjects. In the case of a full-body configuration 482 of a total of 527 (31 x 17) sensors were correctly classified (91.5%). Discussion: Using our algorithm it is possible to create an intelligent sensor, which can determine its own location on the body. The data of the measurements of different daily-life activities is currently being analysed. In addition, we will look into the possibility of simultaneously predicting the on-body location of each sensor and the performed activity.