Drift Reduction for Inertial Sensor Based Orientation and Position Estimation in the Presence of High Dynamic Variability During Competitive Skiing and Daily-Life Walking

Nowadays inertial sensors are extensively used for gait analysis. They can be used to perform temporal event detection (i.e. step detection) and to estimate the orientation of the feet and other body segments to determine walking speed and distance. Usually, orientation is estimated from integration of the measured angular velocity. Prior to integration of measured acceleration to obtain speed, the gravity component has to be estimated and removed. During each integration small measurement errors accumulate and result in so-called drift. Since the first uses of inertial sensors for gait analysis methods have been presented to model, estimate and remove the drift. The proposed methods worked well for relatively slow movements and movements taking place in the sagittal plane. Many methods also relied on periodically occurring static phases such as the stance phase during walking to correct the drift. Inertial sensors could also be used to track higher dynamic movements, for example in sports. Potential applications focus on two aspects: performance analysis and injury prevention. To better explain and predict performance, in-field measurements to assess the coordination, kinematics, and dynamics are key. While traditional movement analysis (e.g. video analysis) can answer most of the questions related to both performance and injury, they are cumbersome and complex to use in-field. Inertial sensors, however, are perfectly suited since they allow to measure the movement in any environment and are not restricted to certain capture volumes. Nevertheless, most sports have very high movement dynamics (e.g. fast direction changes, high speeds) and are therefore challenging for computing reliable estimates of orientation, speed and position. The inertial measurements are compromised by noise and movements oftentimes don't provide static or slow phases used in gait analysis for drift correction. Therefore, the present thesis aimed to propose and validate new methods to model, estimate and remove drift in sports and for movements taking place outdoors in uncontrolled environments. Three different strategies were proposed to measure the movement of classical cross-country skiing and ski mountaineering, alpine ski racing, and outdoor walking over several kilometres. For each activity specific biomechanical constraints and movement dynamics were exploited. The proposed methods rely only on inertial sensors and magnetometers and are able to provide orientation, speed, and position information with an accuracy and precision close to existing gold standards. The most complete system was designed in alpine ski racing, probably one of the most challenging sports for movement analysis. Extreme vibrations, high speeds of over 120 km/h and a timing resolution below 0.01 seconds require maximum accuracy and precision. The athlete's posture and the kinematics of his centre of mass both in a relative athlete-centred frame and in a global Earth-fixed frame could be obtained with high accuracy and precision. Where 3D video analysis requires a very complex experimental setup and takes several hours of post processing to analyse a single turn of a skier, the proposed system allows to measure multiple athletes and complete runs within minutes. Thus, new experimental designs to assess performance and injury risk in alpine ski racing became feasible, greatly helping to gain further knowledge about this highly complex and risky sport

IMPROVING THE ACCURACY OF LOW-COST GNSS BY FUSION WITH INERTIAL AND MAGNETIC SENSORS IN ALPINE SKI RACING

For analysing performance in alpine ski racing, an accurate estimation of the skier's centre of mass trajectory and speed is indispensable. However, the sole use of low-cost GNSS might not be accurate enough to detect meaningful differences. The aim of this study was to introduce a new system that can improve the accuracy of a low-cost GNSS to an acceptable level. To this end, the data obtained by low-cost GNSS was fused with data form inertial sensors and position information of permanent magnets buried into the snow surface along the ski track. This fusion improved the system's accuracy from 2m to 0.5m. Despite the added sensing technologies, the system remained simple and was easy to use. Further improvements are possible and a technical validation of the system could be a major aim for the future

Implementing a Cocktail-party Processor via Time-frequency Masking

The ability of human auditory systems to focus on one signal and ignore other signals in an auditory scene where several auditory events are taking place, often referred to as cocktail-party effect, is a key to localization of sound sources. This ability is partly made possible by interaural cues – Interaural Time Differences (ITDs) and Interaural Level Differences (ILDs) – between the input ear signals that assist the estimation of source azimuth angles, and separation of the signal of the desired direction from signals of non-desire directions. In this paper, we investigate simplified techniques to source separation of sound sources based on inter-channel cues. Particular emphasis is put on the selection of time-frequency masks and its effects on the quality of source separation

USING INERTIAL SENSORS FOR RECONSTRUCTING 3D FULL-BODY MOVEMENT IN SPORTS – POSSIBILITIES AND LIMITATIONS ON THE EXAMPLE OF ALPINE SKI RACING

The present study investigates if inertial sensors could be used for reconstructing 3D full body movements in sports. On the example of alpine ski racing, it was demonstrated that inertial sensors allow computing meaningful parameters related to a skier’s overall posture. While some parameters were obtained with sufficient accuracy and precision, others were not obtained reliably using inertial sensor-based systems. Main error sources were discussed and it was found that an accurate and precise functional calibration is most important for short duration measurements. In cases where it is not possible fixing inertial sensors to all relevant body segments (e.g. skis and arms) their orientations could be estimated. In this case parameter validity needs to be carefully verified, as even strongly related parameters may show different validities, as demonstrated in this study

Stride count and frequency measured with a wrist-worn inertial sensor

This study proposes three simple approaches to estimate the stride count and frequency during walking and running using an inertial measurement system on the wrist. The approaches were based on a time-domain, frequency-domain and autocorrelation analysis, respectively. They were compared and validated against a reference on walks and runs of 16 participants in different conditions (different speeds, over ground and on treadmill). Results showed that the three methods provided an accurate and precise measure of the stride count and frequency: the median stride count error was 1 stride with a 90% confidence interval of 4 strides and the stride frequency presented a median error of 0.03 strides/min with a 90% confidence interval lower than 1.5 strides/min for all three methods. The approach, based on a wrist-worn inertial sensor, offers an effective and simple way to quantify the strides of healthy subjects in various conditions

Improving the accuracy of low-cost GNSS by fusion with inertial and magnetic sensors in alpine ski racing

For analysing performance in alpine ski racing, an accurate estimation of the skier’s centre of mass trajectory and speed is indispensable. However, the sole use of low-cost GNSS might not be accurate enough to detect meaningful differences. The aim of this study was to introduce a new system that can improve the accuracy of a low-cost GNSS to an acceptable level. To this end, the data obtained by low-cost GNSS was fused with data form inertial sensors and position information of permanent magnets buried into the snow surface along the ski track. This fusion improved the system’s accuracy from 2m to 0.5m. Despite the added sensing technologies, the system remained simple and was easy to use. Further improvements are possible and a technical validation of the system could be a major aim for the future

KINETIC AND KINEMATIC COMPARISON OF ALPINE SKI RACING DISCIPLINES AS A BASE FOR SPECIFIC CONDITIONING REGIMES

The purpose of this preliminary case study was to compare the alpine ski racing competition disciplines slalom and giant-slalom with respect to principal kinematics of the lower limbs and the acting forces. Knee angles and ground reaction forces of one high level athlete were determined using inertial sensors and pressure insoles, respectively. Slalom was characterized by a “high dynamic skiing mode” with a distinct “knee angle and loading synchronism” between the inside leg and the outside leg. For giant slalom, a polarized situation was observed: “higher quasi static loads at high knee angles” on the outside leg and “lower eccentric-concentric loads at low knee angles” on the inside leg. These findings may help to increase the specificity of conditioning training and developing more discipline-specific exercises

Estimation of the centre of mass kinematics in alpine ski racing using inertial and magnetic sensors

For performance analysis in alpine ski racing, an accurate and precise estimation of the centre of mass (CoM) kinematics is indispensable. Currently available systems satisfying this need are video-based stereo-photogrammetry or differential global navigation satellite systems (GNSS). However, they are impractical to use in regular training settings. Inertial sensors could be used instead but suffer from significant drifts in speed and position estimation due to the integration of acceleration and angular velocity data. The aim of the present study was to propose and validate an inertial and magnetometer sensor-based algorithm to estimate CoM kinematics in alpine ski racing. Relative CoM position and speed between-run comparisons were found to be highly accurate and precise with mean absolute deviations <0.15 m and <0.28 m/s