Automatic Identification of Inertial Sensors on the Human Body Segments

In the last few years, inertial sensors (accelerometers and gyroscopes) in combination with magnetic sensors was proven to be a suitable ambulatory alternative to traditional human motion tracking systems based on optical position measurements. While accurate full 6 degrees of freedom information is available [1], these inertial sensor systems still have some drawbacks, e.g. each sensor has to be attached to a certain predefined body segment. The goal of this project is to develop a ‘Click-On-and-Play’ ambulatory 3D human motion capture system, i.e. a set of (wireless) inertial sensors which can be placed on the human body at arbitrary positions, because they will be identified and localized automatically

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

Ambulatory gait analysis in stroke patients using ultrasound and inertial sensors

Objective ambulatory assessment of movements of patients is important for an optimal recovery. In this study an ambulatory system is used for assessing gait parameters in stroke patients. Ultrasound range estimates are fused with inertial sensors using an extended Kalman filter to estimate 3D positions, velocities and orientations. For eight stroke patients step lengths and swing and stance times are calculated from a ten meter walk trial and compared to the Berg balance scale. First results show a correlation between step lengths and Berg balance scale score. However, more patients are to be measured and different activities will be investigated in the coming months

A search for transiting planets in the β\beta Pictoris system

The bright $(V=3.86)$ star $\beta$ Pictoris is a nearby young star with a debris disk and gas giant exoplanet, $\beta$ Pictoris b, in a multi-decade orbit around it. Both the planet's orbit and disk are almost edge-on to our line of sight. We carry out a search for any transiting planets in the $\beta$ Pictoris system with orbits of less than 30 days that are coplanar with the planet $\beta$ Pictoris b. We search for a planetary transit using data from the BRITE-Constellation nanosatellite BRITE-Heweliusz, analyzing the photometry using the Box-Fitting Least Squares Algorithm (BLS). The sensitivity of the method is verified by injection of artificial planetary transit signals using the Bad-Ass Transit Model cAlculatioN (BATMAN) code. No planet was found in the BRITE-Constellation data set. We rule out planets larger than 0.6 $\mathrm{R_J}$ for periods of less than 5 days, larger than 0.75 $\mathrm{R_J}$ for periods of less than 10 days, and larger than 1.05 $\mathrm{R_J}$ for periods of less than 20 days.Comment: 6 pages, 6 figures, 3 tables. Accepted for publication in A&

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

Tribute To Professor Ken Margolis

International audienc

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

Ambulatory Estimation of Relative Foot Positions using Ultrasound

The recording of human movement is used for biomedical applications like physical therapy and sports training. Over the last few years inertial sensors have been proven to be a useful ambulatory alternative to traditional optical systems. An example of a successful application is the instrumented shoe, which contains two 6D force/moment sensors beneath the heel and the forefoot and two inertial sensors rigidly attached to the force/moment sensors [1]. These shoes can be used for ambulatory assessment of walking kinetics and kinematics. The relative position of the feet is currently not measured directly but estimated from double integration of feet accelerations. However, this method immediately leads to large position errors (drift) when the estimated inertial accelerations are inaccurate. In this study we investigated the ambulatory estimation of the relative positions of the feet using ultrasound transducers. On one shoe we mounted a 400PT120 Air Ultrasonic Ceramic Transducer (13 mm diameter, 10 mm height, 85º beam angle) sending a 40 kHz pulse to a similar transducer on the other shoe. Using the time of flight, the distance is estimated. Under static conditions a mean error of 5.7 ±0.8 mm was obtained over a range of 5 till 75 cm [2]. From this pilot study we concluded that the distance between the feet can be estimated ambulatory using small and low-cost ultrasound transducers. Future research includes the use of multiple transducers on each foot for a distance measure during different daily-life activities. Also the relative positions of the feet will be investigated by fusing the distance estimates with inertial sensor data