Inertial measurement unit | EPFL Graph Search

An inertial measurement unit (IMU) is an electronic device that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of accelerometers, gyroscopes, and sometimes magnetometers. When the magnetometer is included, IMUs are referred to as IMMUs. IMUs are typically used to maneuver modern vehicles including motorcycles, missiles, aircraft (an attitude and heading reference system), including unmanned aerial vehicles (UAVs), among many others, and spacecraft, including satellites and landers. Recent developments allow for the production of IMU-enabled GPS devices. An IMU allows a GPS receiver to work when GPS-signals are unavailable, such as in tunnels, inside buildings, or when electronic interference is present. Operational principles An inertial measurement unit works by detecting linear acceleration using one or more accelerometers and rotational rate using one or more gyroscopes. Some also include a ma

Monitoring weekly progress of front crawl swimmers using IMU-based performance evaluation goal metrics

Kamiar Aminian, Farzin Dadashi, Vincent Gremeaux, Mahdi Hamidi Rad, Fabien Massé

Technical evaluation of swimming performance is an essential factor in preparing elite swimmers for their competitions. Inertial measurement units (IMUs) have attracted much attention recently because they can provide coaches with a detailed analysis of swimmers' performance during training. A coach can obtain a quantitative and objective evaluation from IMU. The purpose of this study was to validate the use of a new phase-based performance assessment with a single IMU worn on the sacrum during training sessions. Sixteen competitive swimmers performed five one-way front crawl trials at their maximum speed wearing an IMU on the sacrum. The coach recorded the lap time for each trial, as it remains the gold standard for swimmer's performance in competition. The measurement was carried out once a week for 10 consecutive weeks to monitor the improvement in the swimmers' performance. Meaningful progress was defined as a time decrease of at least 0.5 s over a 25 m lap. Using validated algorithms, we estimated five goal metrics from the IMU signals representing the swimmer's performance in the swimming phases (wall push-off, glide, stroke preparation, free-swimming) and in the entire lap. The results showed that the goal metrics for free-swimming phase and the entire lap predicted the swimmer's progress well (e.g., accuracy, precision, sensitivity, and specificity of 0.91, 0.89, 0.94, and 0.95 for the lap goal metric, respectively). As the goal metrics for initial phases (wall push-off, glide, stroke preparation) achieved high precision and specificity (& GE;0.79) in progress detection, the coach can use them for swimmers with satisfactory free-swimming phase performance and make further improvements in initial phases. Changes in the values of the goal metrics have been shown to be correlated with changes in lap time when there is meaningful progress. The results of this study show that goal metrics provided by the phase-based performance evaluation with a single IMU can help monitoring swimming progress. Average velocity of the lap can replace traditional lap time measurement, while phase-based goal metrics provide more information about the swimmer's performance in each phase. This evaluation can help the coach quantitatively monitor the swimmer's performance and train them more efficiently.

FRONTIERS MEDIA SA2022

Toward an objective assessment of mobility in clinical and daily activity settings

Arash Atrsaei

Quantification of mobility is the key to monitor the progression of mobility disorders as well as the effect of an intervention. Inertial measurement units (IMUs) with dedicated algorithms can quantify postural transitions and gait as the two key aspects of mobility in an objective and continuous manner. IMU-based mobility assessments can be performed by either functional tests in the clinic or through daily activities. Assessments performed in the clinic are more indicative of peoples best performance or capacity, while assessments performed at home represent mostly their actual performance. Yet the relationship between these two settings is not fully understood, both due to the existing gaps in technical algorithms as well as challenges in comparing two inherently different domains. To this end, in this thesis, I firstly focused on developing and validating algorithms to quantify mobility in both clinical and domestic environments. The added clinical value of these IMU-based mobility assessments was shown in several populations with mobility impairments. Finally, by proposing novel approaches, I focused to bridge the gap between clinical and daily activity assessments. The previous approaches to quantify mobility are mostly based on algorithms that are validated only during clinical or lab-based assessments. Opposed to daily activities, lab assessments contain simple and single-task activities. Therefore, it is important to design algorithms robust to the complex context of daily life setting while being unobtrusive to daily activities. A new algorithm was introduced to detect and characterize postural transitions, i.e., sitting and standing. Next, machine-learning-based algorithms were developed to detect walking bouts and estimate gait speed. The proposed postural transition and gait quantification algorithms were based on a single IMU on the lower back which is unobtrusive to daily activities. The novelty of the algorithms is their robustness to sensor placement changes during daily activities. The proposed algorithms demonstrated high performance during both clinical and daily activity assessments whether on healthy individuals or participants with mobility impairments. Next, through several analyses, I demonstrated how such instrumented mobility assessments can discriminate different patient populations. For instance, IMU-derived mobility parameters could differentiate older adults with and without risk of falls as well as patients with moderate or severe stages of multiple sclerosis. Moreover, the aforementioned parameters were compared between clinical and daily activity assessments. By this comparison, clinicians can have a better understanding of patients capacity through a remote assessment of mobility. Finally, it was shown how clinical and daily activity assessments can provide complementary information to each other. For instance, by introducing novel approaches to compare gait speed between clinical and daily activity assessments, the effect of the medication in Parkinsons disease (PD) was traceable during daily activities. The findings can lead to better optimization of the medication dose in PD. Overall, this thesis provided a framework that can help clinicians with an objective assessment of mobility. Furthermore, the approaches introduced in this thesis can help for better management of intervention and tracking its effects where both clinical and daily activity assessments exist.

EPFL2021

Modelling, Simulations and Characterization of NEMS accelerometers based on graphene

Daniel Moreno Garcia

Accelerometers are widely used in industrial applications and consumer electronics. We can find them in automotive crash detection or fitness trackers. The majority are based on piezoresistive or capacitive effect which are limited by their large size. This negatively influences their utility in emerging applications such as Internet of Things and biomedical applications. NEMS (Nanoelectromechanical systems) are presented as a possible solution for this problem. The accelerometers used in this project are made of a suspended graphene membrane and an attached silicon proof mass. They were fabricated by KTH researchers. We study the possibility of measuring accelerations by monitoring the changes in the device resonant frequency. If proven, to the best of our knowledge, they would be the first ultra-miniaturized and sensitive resonant accelerometer. To do so, we built a functioning model of the accelerometers by combining a theoretical framework and finite element simulations. The chip was placed on a piezo-shaker and measurements were conducted using a Laser Doppler Vibrometer. To complement the study, further measures were taken with an Atomic Force Microscope and a Digital Holographic Microscope. The results show a good match between theory and simulations. However, the acceleration measurements in the device show higher signals not related with accelerations. We proved they were related to displacements and theorized that they were caused by internal chip forces. Horizontal forces on the accelerometer frame could eclipse the effect of the accelerations on the resonant frequency. We suggest the use of a more rigid chip frame as a potential solution.

2020