Estimating Body Segment Orientation by Applying Inertial and Magnetic Sensing Near Ferromagnetic Materials

Inertial and magnetic sensors are very suitable for ambulatory monitoring of human posture and movements. However, ferromagnetic materials near the sensor disturb the local magnetic field and, therefore, the orientation estimation. A Kalman-based fusion algorithm was used to obtain dynamic orientations and to minimize the effect of magnetic disturbances. This paper compares the orientation output of the sensor fusion using three-dimensional inertial and magnetic sensors against a laboratory bound opto-kinetic system (Vicon) in a simulated work environment. With the tested methods, the difference between the optical reference system and the output of the algorithm was 2.6deg root mean square (rms) when no metal was near the sensor module. Near a large metal object instant errors up to 50deg were measured when no compensation was applied. Using a magnetic disturbance model, the error reduced significantly to 3.6deg rms

Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation

This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p<0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4/spl deg/ (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6/spl deg/ root means square

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

Ambulatory estimation of foot placement during walking using inertial sensors

This study proposes a method to assess foot placement during walking using an ambulatory measurement system consisting of orthopaedic sandals equipped with force/moment sensors and inertial sensors (accelerometers and gyroscopes). Two parameters, lateral foot placement (LFP) and stride length (SL), were estimated for each foot separately during walking with eyes open (EO), and with eyes closed (EC) to analyze if the ambulatory system was able to discriminate between different walking conditions. For validation, the ambulatory measurement system was compared to a reference optical position measurement system (Optotrak). LFP and SL were obtained by integration of inertial sensor signals. To reduce the drift caused by integration, LFP and SL were defined with respect to an average walking path using a predefined number of strides. By varying this number of strides, it was shown that LFP and SL could be best estimated using three consecutive strides. LFP and SL estimated from the instrumented shoe signals and with the reference system showed good correspondence as indicated by the RMS difference between both measurement systems being 6.5±1.0 mm (mean ±standard deviation) for LFP, and 34.1±2.7 mm for SL. Additionally, a statistical analysis revealed that the ambulatory system was able to discriminate between the EO and EC condition, like the reference system. It is concluded that the ambulatory measurement system was able to reliably estimate foot placement during walking.\ud \u

Cervical Muscle Dysfunction in the Chronic Whiplash Associated Disorder Grade II (WAD-II)

Study Design: In a cross-sectional study, surface electromyography\ud measurements of the upper trapezius\ud muscles were obtained during different functional tasks in\ud patients with a chronic whiplash associated disorder\ud Grade II and healthy control subjects. - \ud Objectives: To investigate whether muscle dysfunction\ud of the upper trapezius muscles, as assessed by surface\ud electromyography, can be used to distinguish patients\ud with whiplash associated disorder Grade II from\ud healthy control subjects. - \ud Summary of Background Information: In the whiplash\ud associated disorder, there is need to improve the diagnostic\ud tools. Whiplash associated disorder Grade II is\ud characterized by the presence of “musculoskeletal signs.”\ud Surface electromyography to assess these musculoskeletal\ud signs objectively may be a useful tool. - \ud Methods: Normalized smoothed rectified electromyography\ud levels of the upper trapezius muscles of patients\ud with whiplash associated disorder Grade II (n 5 18) and\ud healthy control subjects (n 5 19) were compared during\ud three static postures, during a unilateral dynamic manual\ud exercise, and during relaxation after the manual exercise.\ud Coefficients of variation were computed to identify the\ud measurement condition that discriminated best between\ud the two groups. - \ud Results: The most pronounced differences between\ud patients with whiplash associated disorder Grade II and\ud healthy control subjects were found particularly in situations\ud in which the biomechanical load was low. Patients\ud showed higher coactivation levels during physical exercise\ud and a decreased ability to relax muscles after physical\ud exercise. - \ud Conclusions: Patients with whiplash associated disorder\ud Grade II can be distinguished from healthy control\ud subjects according to the presence of cervical muscle\ud dysfunction, as assessed by surface electromyography of\ud the upper trapezius muscles. Particularly the decreased\ud ability to relax the trapezius muscles seems to be a promising\ud feature to identify patients with whiplash associated\ud disorder Grade II. Assessment of the muscle (dys)function\ud by surface electromyography offers a refinement of the\ud whiplash associated disorder classification and provides\ud an indication to a suitable therapeutic approach. [Key\ud words: whiplash associated disorder, muscle dysfunction,\ud surface electromyography, upper trapezius muscle,\ud static, dynamic, relaxation

Optimizing Activity Recognition in Stroke Survivors for Wearable Exoskeletons

Stroke affects the mobility, hence the quality of life of people victim of this cerebrovascular disease. Part of research has been focusing on the development of exoskeletons bringing support to the user's joints to improve their gait and to help regaining independence in daily life. One example is Xosoft, a soft modular exoskeleton currently being developed in the framework of the European project of the same name. On top of its assistive properties, the soft exoskeleton will provide therapeutic feedback via the analysis of kinematic data stemming from inertial sensors mounted on the exoskeleton. Prior to these analyses however, the activities performed by the user must be known in order to have sufficient behavioral context to interpret the data. Four activity recognition chains, based on machine learning algorithm, were implemented to automatically identify the nature of the activities performed by the user. To be consistent with the application they are being used for (i.e. Wearable exoskeleton), focus was made on reducing energy consumption by configuration minimization and bringing robustness to these algorithms. In this study, movement sensor data was collected from eleven stroke survivors while performing daily-life activities. From this data, we evaluated the influence of sensor reduction and position on the performances of the four algorithms. Moreover, we evaluated their resistance to sensor failures. Results show that in all four activity recognition chains, and for each patient, reduction of sensors is possible until a certain limit beyond which the position on the body has to be carefully chosen in order to maintain the same performance results. In particular, the study shows the benefits of avoiding lower legs and foot locations as well as the sensors positioned on the affected side of the stroke patient. It also shows that robustness can be brought to the activity recognition chain when the data stemming from the different sensors are fused at the very end of the classification process