A Feasibility Study in Measuring Soft Tissue Artifacts on the Upper Leg Using Inertial and Magnetic Sensors

Soft-tissue artifacts cause inaccurate estimates of body segment orientations. The inertial sensor (or optical marker) is orientating (or displacing) with respect to the bone it has to measure, due to muscle and skin movement [1]. In this pilot study 11 inertial and magnetic sensors (MTw, Xsens Technologies) were placed on the rectus femoris, vastus medialis and vastus lateralis (upper leg). One sensor was positioned on the tendon plate behind the quadriceps (iliotibial tract, as used in Xsens MVN [1]) and used as reference sensor. Walking, active and passive knee extensions and muscle contractions without flexion/extension were recorded using one subject. The orientation of each sensor with respect to the reference sensor was calculated. During walking, relative orientations of up to 28.6º were measured (22.4±3.6º). During muscle contractions without flexion/extension the largest relative orientations were measured on the rectus femoris (up to 11.1º) [2]. This pilot showed that the ambulatory measurement of deformation of the upper leg is feasible; however, improving the measurement technology is required. We therefore have designed a new inertial and magnetic sensor system containing smaller sensors, based on the design of an instrumented glove for the assessment of hand kinematics [3]. This new sensor system will then be used to investigate soft-tissue artifacts more accurately; in particular we will focus on in-use estimation and elimination of these artifacts

Pre-operative ambulatory measurement of asymmetric lower limb loading during walking in total hip arthroplasty patients

The main goal of this study was to investigate how mobility characteristics during walking, relate to gait velocity and questionnaire outcomes of patients with hip osteoarthritis in an outpatient setting. Methods 22 patients with primary osteoarthritis of the hip selected for a total hip arthroplasty participated in this study. For each patient the Harris Hip Score, the Traditional Western Ontario and the McMaster Universities osteoarthritis index were administered. Subsequently, the patients were instructed to walk through the corridor while wearing instrumented shoes. The gait velocity estimated with the instrumented force shoes was validated measuring the time required to walk a distance of 10 m using a stopwatch and a measuring tape as a reference system. A regression analysis between spatial, temporal, ground reaction force parameters, including asymmetry, and the gait velocity and the questionnaires outcomes was performed

On-body inertial sensor location recognition

Introduction and past research:\ud 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. \ud \ud Method:\ud 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. \ud \ud Results:\ud 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%). \ud \ud Discussion:\ud 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

Gait analysis using ultrasound and inertial sensors

Introduction and past research:\ud Inertial sensors are great for orientation estimation, but they cannot measure relative positions of human body segments directly. In previous work we used ultrasound to estimate distances between body segments [1]. In [2] we presented an easy to use system for gait analysis in clinical practice but also in-home situations. Ultrasound range estimates were fused with data from foot-mounted inertial sensors, using an extended Kalman filter, for 3D (relative) position and orientation estimation of the feet.\ud \ud Validation:\ud From estimated 3D positions we calculated step lengths and stride widths and compared this to an optical reference system for validation. Mean (±standard deviation) of absolute differences was 1.7 cm (±1.8 cm) for step lengths and 1.2 cm (±1.2 cm) for stride widths when comparing 54 walking trials of three healthy subjects.\ud \ud Clinical application:\ud Next, the system presented in [2] was used in the INTERACTION project, for measuring eight stroke subjects during a 10 m walk test [3]. Step lengths, stride widths and stance and swing times were compared with the Berg balance scale score. The first results showed a correlation between step lengths and Berg balance scale scores. To draw real conclusions, more patients and also different activities will be investigated next.\ud \ud Future work:\ud In future work we will extend the system with inertial sensors on the upperand lower legs and the pelvis, to be able to calculate a closed loop and improve the estimation of joint angles compared with systems containing only inertial sensors

Pre-operative ambulatory measurement of asymmetric leg loading during sit to stand in hip arthroplasty patients

Total hip arthroplasty is a successful surgical procedure to treat patients with hip osteoarthritis. Clinicians use different questionnaires to evaluate these patients. Gait velocity and these questionnaires; usually show significant improvement after total hip arthroplasty. This clinical evaluation does, however, not provide objective, quantifiable information about the movement patterns underlying the functional capacity, which is clinically important and can currently only be obtained in a gait laboratory. There is a need to improve patient instructions and to quantify the rehabilitation process. The sit to stand (STS) movement is an objective performance-based task, whose assessment is related with the evaluation of functional recovery. Twenty two patients with hip osteoarthritis participated in this study. For each patient, validated questionnaires were administered and gait velocity was measured. Time, ground reaction forces and lower limb asymmetry parameters were calculated using the Instrumented Force Shoes (IFS) during STS movement with and without armrest. Significant inter-limb asymmetry was observed. No correlation was found between any parameter and gait velocity and questionnaires outcomes. Significant differences in time and force parameters between with/without armrest were found. Concluding, inter-limb asymmetry can be evaluated with the IFS supplying important additional information not represented by gait velocity and questionnaires usually used

Ambulatory estimation of relative foot positions by fusing ultrasound and inertial sensor data

Relative foot position estimation is important for rehabilitation, sports training and functional diagnostics. In this paper an extended Kalman filter fusing ultrasound range estimates and inertial sensors is described. With this filter several gait parameters can be estimated ambulatory. Step lengths and stride widths from 54 walking trials of three healthy subjects were estimated and compared to an optical reference. Mean ( standard deviation) of absolute difference was 1.7 cm (1.8 cm) and 1.2 cm (1.2 cm) for step length and stride width respectively. Walking with a turn and walking around in a square area were also investigated and resulted in mean absolute differences of 1.7 cm (2.0 cm) and 1.5 cm (1.5 cm) for step lengths and stride widths. In addition to these relative positions, velocities, orientations and stance and swing times can also be estimated. We conclude that the presented system is low-cost and provides a complete description of footstep kinematics and timing