Laetitia Fradet

A personalized exercise trainer for the elderly

Physical activity provides many physiological benefits. On the one hand it reduces the risk of disease outcomes. On the other hand it is the basis for proper rehabilitation in case of or after a severe disease. Both aspects are especially important for the elderly population. Within this context, the present paper proposes a personalized, home-based exercise trainer for elderly people. The system is based on a wearable sensor network that enables capturing the users motions. These are then evaluated by comparing them to a prescribed exercise, taking both exercise load and technique into account. Moreover, the results are translated into appropriate feedback to the user to assist the correct exercise execution. A novel part of the system is the generic personalization by means of a supervised teach-in phase.

Comparison of calibration methods for accelerometers used in human motion analysis.

In the fields of medicine and biomechanics, MEMS accelerometers are increasingly used to perform activity recognition by directly measuring acceleration; to calculate speed and position by numerical integration of the signal; or to estimate the orientation of body parts in combination with gyroscopes. For some of these applications, a highly accurate estimation of the acceleration is required. Many authors suggest improving result accuracy by updating sensor calibration parameters. Yet navigating the vast array of published calibration methods can be confusing. In this context, this paper reviews and evaluates the main measurement models and calibration methods. It also gives useful recommendations for better selection of a calibration process with regard to a specific application, which boils down to a compromise between accuracy, required installation, algorithm complexity, and time.

Personalized Physical Activity Monitoring Using Wearable Sensors

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

Revue de la littératureModifications de la statique pelvienne et de la laxité ligamentaire pendant la grossesse et le post-partum. Revue de la littérature et perspectivesChanges in pelvic organ mobility and ligamentous laxity during pregnancy and postpartum. Review of literature and prospects

INTRODUCTION The role of pregnancy in pelvic floor disorders occurrence remains poorly known. It might exist a link between changes in ligamentous laxity and changes in pelvic organ mobility during this period. Our objective was to conduct a non-systematic review of literature about changes in pelvic organ mobility as well as in ligamentous laxity during pregnancy and postpartum. METHODS From the PubMed, Medline, Cochrane Library and Web of Science database we have selected works which pertains clinical assessment of pelvic organ mobility (pelvic organ prolapse quantification), ultrasound assessment of levator hiatus and urethral mobility, ligamentous laxity assessment during pregnancy and postpartum. RESULTS Clinical assessments performed in these works show an increase of pelvic organ mobility and perineal distension during pregnancy followed by a recovery phase during postpartum. Pelvic floor imaging shows an increase of levator hiatus area and urethral mobility during pregnancy then a recovery phase in postpartum. Different authors also report an increase of ligamentous laxity (upper and lower limbs) during pregnancy followed by a decrease phase in postpartum. CONCLUSION Pelvic organ mobility, ligamentous laxity, levator hiatus and urethral mobility change in a similarly way during pregnancy (increase of mobility or distension) and postpartum (recovery). LEVEL OF EVIDENCE 3.

Modifications de la statique pelvienne et de la laxité ligamentaire pendant la grossesse et le post-partum. Revue de la littérature et perspectives

INTRODUCTION The role of pregnancy in pelvic floor disorders occurrence remains poorly known. It might exist a link between changes in ligamentous laxity and changes in pelvic organ mobility during this period. Our objective was to conduct a non-systematic review of literature about changes in pelvic organ mobility as well as in ligamentous laxity during pregnancy and postpartum. METHODS From the PubMed, Medline, Cochrane Library and Web of Science database we have selected works which pertains clinical assessment of pelvic organ mobility (pelvic organ prolapse quantification), ultrasound assessment of levator hiatus and urethral mobility, ligamentous laxity assessment during pregnancy and postpartum. RESULTS Clinical assessments performed in these works show an increase of pelvic organ mobility and perineal distension during pregnancy followed by a recovery phase during postpartum. Pelvic floor imaging shows an increase of levator hiatus area and urethral mobility during pregnancy then a recovery phase in postpartum. Different authors also report an increase of ligamentous laxity (upper and lower limbs) during pregnancy followed by a decrease phase in postpartum. CONCLUSION Pelvic organ mobility, ligamentous laxity, levator hiatus and urethral mobility change in a similarly way during pregnancy (increase of mobility or distension) and postpartum (recovery). LEVEL OF EVIDENCE 3.

Position for labor and birth: State of knowledge and biomechanical perspectives

This review aims to examine how childbirth position during labour affects maternal, fetal and neonatal outcomes. Epidemiological data suggest that vertical birthing positions have many benefits. But when we consider the players and mechanisms of delivery, including the forces generated to move the fetus and obstacles to its progression, many questions remain about the advantage of one position over another. Thus, childbirth could be considered in a way as an athletic feat that probably requires the choice of optimal positions. These should be individually suited to each woman at different stage of labour to improve its efficiency and effectiveness. Tweetable abstract: Beyond epidemiological data, biomechanical investigations is necessary to assess births position.

Simple and efficient thermal calibration for MEMS gyroscopes

Gyroscopes are now becoming one of the most sold MEMS sensors, given that the many applications that require their use are booming. In the medical field, gyroscopes can be found in Inertial Measurement Units used for the development of clinical tools that are dedicated to human-movement monitoring. However, MEMS gyroscopes are known to suffer from a drift phenomenon, which is mainly due to temperature variations. This drift dramatically affects measurement capability, especially that of cheap MEMs gyroscopes. Calibration is therefore a key factor in achieving accurate measurements. However, traditional calibration procedures are often complex and require costly equipment. This paper therefore proposes an easy protocol for performing a thermal gyroscope calibration. In this protocol, accuracy over the angular velocity is evaluated by referring to an optoelectronic measurement, and is compared with the traditional calibration performed by the manufacturer. The RMSE between the reference angular velocity and that obtained with the proposed calibration was of 0.7°/s, which was slightly smaller than the RMSE of 1.1°/s achieved by the manufacturers calibration. An analysis of uncertainty propagation shows that offset variability is the major source of error over the computed rate of rotation from the tested sensors, since it accounts for 97% of the error. It can be concluded that the proposed simple calibration method leads to a similar degree of accuracy as that achieved by the manufacturers procedure.

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.

Movement analysis with inertial sensors: identification of noise covariance matrices to improve body segment orientation using Kalman filtering

In this context, inertial measurement units (IMU) are a promising way to perform human motion analysis out of the lab, with no need for external devices. However, IMUs do not measure directly the orientation but only acceleration, rotation rate, and magnetic field. These data have thus to be converted in order to estimate the orientation. Moreover, IMUs designed for human motion analysis are based on MEMS technology which allows sensors to be cheap and small but which also induces noise and bias over the measurement. In the literature, Kalman filtering is recognized as one of the best tool to perform data fusion from noised measurement. The main idea is to combine the sensors measurement with a dynamic model that translates the time-behaviour of the state to be estimated. However, there is no consensus on the structure of the algorithm and its settings. A critical point when implementing a Kalman filter is the definition of the two covariance matrices that characterize mismodelling and input error from noisy sensors. There is no consensus in the literature on a method to define these covariance matrices, even if it is crucial since it can lead to inaccurate estimation as well as divergence and stability problems (Foxlin 1996). The aim of this study is to provide a solution to identify input parameters that optimize orientation estimation. 2. Methods

Which functional movements for sensor-to-segment calibration for lower-limb movement analysis with inertial sensors?

If human motion analysis in laboratory can be considered as matured regarding the extensive research that has been performed in terms of technology and methodology, it is not the case for human motion analysis based on inertial sensors. Basically, for 3D movement analysis, angular velocities and linear accelerations measured by respectively the 3 gyroscopes and the 3 accelerometers present in one 3D-Inertial Measurement Unit (IMU) are sent to a data fusion algorithm in order to obtain orientation and, sometimes, also position (see Picerno 2017 for a review). Several challenges remain to be solved for this technology to be really effective and spread and this includes the sensor-to-segment calibration. Indeed, to estimate joint kinematics, the rotational matrix that enables to obtain the body segment referential frame orientation from that of the sensor is required for each sensor. Different procedures have been proposed in the literature (Picerno 2017). Some of them use static postures whereas others use devices to locate anatomical landmarks required to define segment axes relatively to the sensor referential frame. Unfortunately, these approaches are limited in terms of accuracy or require additional equipment. With the “functional approach”, specific movement performed at a joint is used to define a segment axis assuming that the angular velocity vector is aligned with the segment axis around which the movement is performed. Even if not yet demonstrated, this functional approach seems to provide the best ratio between time, ease, and accuracy. However, none of the functional approaches is currently recognized as a ‘‘gold standard’’. These procedures should then be more thoroughly investigated in order to define their advantages and drawbacks. We propose in the present study to investigate the calibration movements for lower-limbs sensor-to segment calibration. Indeed, the accuracy of the functional approach is limited by the precision with which the subjects can perform the motions, which is of particular importance to define axes in segment such as the thigh segment. In the present study, the segment axes will be defined with angular velocity vectors, not measured by inertial sensors but deduced from markers tracked by an optoelectronical system. The segment axes will then be confronted with those obtained by validated functional approach and model that have been proposed in traditional 3D motion analysis based on optoelectronical systems. To consider kinematics measures, models and methods based on optoelectronical systems as the gold standard enables to limit uncertainties due to lack of studies on human movement analysis based on inertial sensors (such as sensor locations for instance).