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Illustration of the wearable motion analysis based on biomechanical simulations and sensor data synthesis. (A) Illustration of the sensor-extended, personal full-body model while running, including 834 simulated sensors uniformly positioned at upper and lower arms and legs, as well as feet. (B) Feet models with an inertia-free shoe model used to place sensor models. The vertical calcaneus position in y-axis orientation served as stride reference. (C) Example of the acceleration time series data synthesised for each simulated sensor. (D) Zoomed view of the lower arm (64 sensors), and the upper leg (96 sensors). For visualisation of the sensor positions, the femur was rotated. (E) Normalised root-mean-square-error (nRMSE) of the marker stride duration, derived between simulated sensors and the calcaneus reference. Colour-coded nRMSE maps shown here for athlete ID5.
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We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us...
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... used the dataset from Hamner et al. 24 , including motion data recordings of each athlete running on a treadmill with 2 m/s, 3 m/s, 4 m/s, and 5 m/s. Figure 2 illustrates signals and performance estimates for the gait marker stride duration, depending on sensor position at the lower arm and the upper leg of one athlete. To visualise the varying marker performance, we show normalised root-mean-square-error (nRMSE) maps across body positions. ...
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... simulated sensors and all running speeds. Table 1 summarises stride durations derived from the best simulated sensor using the cmplx algorithm at each body region and the calcaneus reference. We derived the heel strike reference from the vertical position of the calcaneus during the ground contact and segmented individual strides accordingly (see Fig. 2). Stride duration std. dev. at the calcaneus reference is approx. 100 ms. Relative errors between calcaneus reference and simulated sensors increase with running speed. For the athletes investigated in this study, left and right body side show similar nRMSE, indicating that gait patterns were symmetric. The analysis furthermore shows ...
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... Case Study 1, our approach demonstrated that increasing running speed in athletes increases marker estimation error from 0.1 to 11.6% on average. We investigated body positions that are frequently considered by runners for wearing sensor devices, as shown in Fig. 2, including the upper arm (e.g. music player or smartphone in an arm-holder), the lower arm (e.g. wrist-worn devices, smartwatches), the foot (e.g. for shoe and shoe-tongue integrated sensors), as well as the upper and lower leg (e.g. for wearable device straps). Estimates at upper legs, feet, and upper arms showed lower average nRMSE ...
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Citations
... This variation was found to be considerably higher than the baseline variation observed under static conditions, in which case this variation likely resulted from electrical noise (Fig. 2c). Therefore, similarly to previous work, amplitude thresholding was considered to detect the sharp contact-induced artefacts [18][19][20][21][22][23][24][25][26][27][28][29][30] . The impact of these artefacts was then assessed under the three different levels of motion. ...
Conventional snap fasteners used in clothing are often used as electrical connectors in e-textile and wearable applications for signal transmission due to their wide availability and ease of use. Nonetheless, limited research exists on the validation of these fasteners, regarding the impact of contact-induced high-amplitude artefacts, especially under motion conditions. In this work, three types of fasteners were used as electromechanical connectors, establishing the interface between a regular sock and an acquisition device. The tested fasteners have different shapes and sizes, as well as have different mechanisms of attachment between the plug and receptacle counterparts. Experimental evaluation was performed under static conditions, slow walking, and rope jumping at a high cadence. The tests were also performed with a test mass of 140 g. Magnetic fasteners presented excellent electromechanical robustness under highly dynamic human movement with and without the additional mass. On the other hand, it was demonstrated that the Spring snap buttons (with a spring-based engaging mechanism) presented a sub-optimal performance under high motion and load conditions, followed by the Prong snap fasteners (without spring), which revealed a high susceptibility to artefacts. Overall, this work provides further evidence on the importance and reliability of clothing fasteners as electrical connectors in wearable systems.
... Similarly, in sports and fitness applications, wearable sensors play a crucial role in tracking performance metrics like speed, distance, and acceleration. (b) A study by Derungs and Amft [2] looked into the use of wearable sensors to track and examine athletes' biomechanics during sprinting. (c) Moreover, wearable sensors have proven instrumental in rehabilitation settings by monitoring patients' movements and providing feedback to enhance their motor skills. ...
Wearable sensors offer great potential in sports, fitness, and medicine. However, their limited battery life poses a major obstacle to their widespread use. This paper explores various energy solutions to extend the battery life of wearable sensors. The first part of the paper focuses on hardware improvements for wearable sensors, such as employing low-power sensors, energy-efficient micro-controllers, and power management circuits. The second part discusses the utilization of solar, thermal, and kinetic energy harvesting techniques to power wearable sensors. These methods aim to harness energy from the environment to supplement or replace battery power. The third part examines wireless power transfer techniques like radio frequency and magnetic resonance, which enable continuous power supply to wearable sensors without the need for physical connections. Software optimization is also highlighted as a crucial aspect in reducing energy consumption. This involves developing efficient algorithms for data processing and transmission, as well as implementing sleep and wake-up modes to minimize power usage during idle periods. Furthermore, the paper emphasizes the importance of user interaction and behavior in improving energy efficiency. It suggests the development of user-friendly interfaces and providing users with feedback on their energy usage.
... Wearable inertial measurement units (IMUs), among other measuring methods, enable researchers in biomechanics and rehabilitation to measure human kinematics and therefore expand our understanding of natural human movement (Derungs and Amft, 2020;Al Borno et al., 2022). In medicine, movement applications range from analysing walking performance (Balasubramanian et al., 2009), detecting functional impairment in patients after a stroke (Balaban and Tok, 2014), regaining walking autonomy (Canning et al., 2006), to monitoring fall risk in older adults and patients (Tulipani et al., 2020). ...
... Simulationbased analyses may offer an excellent tool to systematically address various challenges related to finding suitable sensor configurations and signal interpretation methods from the extensive design space and parametrisation options of wearable IMU systems. Additionally, inter-individual characteristics and coping strategies substantially influence wearable system design and performance of sensor-based movement interpretation (Altini et al., 2015;Derungs and Amft, 2020). For a given wearable application, error effects related to sensor type, body positioning, parametrisation of signal interpretation algorithms, etc., are to date solely available as expert knowledge, if at all. ...
... Modeling and simulation methods could enable researchers and practitioners to virtually select, augment, and exchange sensor devices, body positions, as well as to test different modalities and signal interpretation algorithms without patient involvement. Derungs and Amft (2020) performed a first quantitative analysis of gait performance estimation from acceleration signals synthesised in a co-simulation of human body and sensor models. While the work demonstrated that simulation can reveal the full potential of wearable motion sensors and increase their application in clinical practice, the work only focused on dynamic acceleration of the patient's upper leg and did not cover gait phase events. ...
We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.
... Running while minimizing footfall sound volume induces reductions in vertical impact forces [27,29], as runners adjust their leg stiffness to accommodate mechanical demands [39]. However, the impact forces generated during initial contact are dissipated, especially through the pelvis and the spine [36]. ...
Accelerometry is becoming a popular method to access human movement in outdoor conditions. Running smartwatches may acquire chest accelerometry through a chest strap, but little is known about whether the data from these chest straps can provide indirect access to changes in vertical impact properties that define rearfoot or forefoot strike. This study assessed whether the data from a fitness smartwatch and chest strap containing a tri-axial accelerometer (FS) is sensible to detect changes in running style. Twenty-eight participants performed 95 m running bouts at ~3 m/s in two conditions: normal running and running while actively reducing impact sounds (silent running). The FS acquired running cadence, ground contact time (GCT), stride length, trunk vertical oscillation (TVO), and heart rate. Moreover, a tri-axial accelerometer attached to the right shank provided peak vertical tibia acceleration (PKACC). The running parameters extracted from the FS and PKACC variables were compared between normal and silent running. Moreover, the association between PKACC and smartwatch running parameters was accessed using Pearson correlations. There was a 13 ± 19% reduction in PKACC (p < 0.005), and a 5 ± 10% increase in TVO from normal to silent running (p < 0.01). Moreover, there were slight reductions (~2 ± 2%) in cadence and GCT when silently running (p < 0.05). However, there were no significant associations between PKACC and the variables extracted from the FS (r < 0.1, p > 0.05). Therefore, our results suggest that biomechanical variables extracted from FS have limited sensitivity to detect changes in running technique. Moreover, the biomechanical variables from the FS cannot be associated with lower limb vertical loading.
... Biomechanical models can provide a basis for evaluating wearable system design, e.g. to analyse the performance of digital biomarker estimation algorithms depending on the wearable sensor position [1]. Validated musculoskeletal models are provided, e.g. by the open-source OpenSim platform (https://simtk.org/projects/opensim) ...
... Figure 3E and Fig. 1D illustrate all body areas of interest. Based on [1] for skeletal models, sensor cube positions on arms and legs were adjusted to body areas of interest around approximate physical IMU positions (see Fig. 1A). ...
... The results were also impacted by the human motion capture test artefact placement and movement during the task. Recent studies have shown that wearable sensors and marker clusters can also be sensitive to placement location, and that using a biomechanical model to optimise location of wearable sensors, potentially similar to human motion capture test artefacts, for an individual can improve the results (Derungs and and Amft, 2020). ...
... Human motion detection using flexible and wearable devices has been a popular academic research topic regarding smart wearable electronics for a long time. Understanding physical activity by monitoring body motion is important for basic health management, and many interesting and practicable studies have been reported [104][105][106]. Early in 1953, an acceleration sensor was first employed in human motion detection, and with the development of a micro-electromechanical systems technique, acceleration sensors have become miniature, lightweight, cheaper, and more intelligent to better cater for practical market applications. ...
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... IMUSim [9] first systematically introduced a method to generate synthetic acceleration, rotation velocity, and magnetic data with a Motion Capture (MoCap)-based human movement trajectory. This approach was mainly adopted for subsequent applications, for example, training the ML network from virtual avatar motion in Unity3D [10], gait analysis [29][30][31], and pedestrian trajectory [32]. To investigate motion with the fine-grained virtual sensor, Derungs et al. [29] attached plenty of virtual IMU sensors to a virtual avatar. ...
... This approach was mainly adopted for subsequent applications, for example, training the ML network from virtual avatar motion in Unity3D [10], gait analysis [29][30][31], and pedestrian trajectory [32]. To investigate motion with the fine-grained virtual sensor, Derungs et al. [29] attached plenty of virtual IMU sensors to a virtual avatar. However, they only considered gait motion, such as running, to analyze different sensor position effects on the gait marker parameters. ...
Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the optimal sensor position from all possible locations under a given sensor number. Utilizing virtual sensor data, the training dataset can be accessed at low cost. The system can help the decision-making process of sensor position selection with great accuracy using feedback, as well as output the classifier at a lower cost than a conventional training model.
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