Christina Strohrmann

Monitoring Kinematic Changes With Fatigue in Running Using Body-Worn Sensors

In this paper, we investigate monitoring of kinematic changes evoked by fatigue in running using wearable technology. Movement data were recorded with ETHOS devices. ETHOS is the ETH Orientation Sensor, a customized inertial measurement unit for unconstrained monitoring of human movement. We perform two real-world experiments, in which 21 runners of different skill levels participated. The real-world experiments capture two exhausting 45-min runs: one on a treadmill and one on a conventional outdoor track. We describe and evaluate algorithms to extract kinematic parameters from the sensor data. We identified parameters that change with fatigue for all runners, ones that change for runners of distinct skill levels, and ones that are dependent on an individuals running technique. Overall, we found that observations from treadmill running are not always generalizable to outdoor running. We, thus, argue for using wearable technology to provide athletes and trainers with continuous, quantitative objective measurements of running technique. These could be used to further gain insight into the complex relationship of running kinematics, injury risk, fatigue, and running economy.

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

BackgroundRehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients’ feedback and experts’ observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.MethodsWe performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.ResultsWe achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p<0.05 for all tasks) between our regression estimate of motor task capacity and experts’ ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.ConclusionsWe found that children’s task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children’s motor performance in daily life with a follow-up study.

What Do Sensors Know about Your Running Performance

About 489 000 athletes have finished a marathon in the US in 2009, the average training distance of athletes is 47.5 km per week. While average fitness runners (55.8%) train by self perception, a systematic assessment of kinematic parameters is limited to elite athletes that have access to instrumented environments. This work investigates the potential of wearable sensors to derive kinematic features in running. We equipped 12 runners of different performance levels with each 12 miniature ETHOS units. ETHOS constitutes a miniature inertial measurement unit (IMU) that is optimized for long term monitoring in unconstrained environments. We found that a minimum set of two acceleration sensors attached to the athletes foot and hip is sufficient to derive kinematic features that allow for distinction of experienced and unexperienced runners. Our work constitutes a first step towards personal training assistance providing runners kinematic metrics for performance improvement and injury risk reduction.

Can Smartphones Help with Running Technique

Abstract Running is one of the most popular sports for the masses. However, not every runner might run properly. Incorrect running technique decreases movement efficiency and increases the risk of injury. In this work, we present the development of a smartphone application to provide feedback on running technique on the example of arm carriage. Recognition algorithms were developed in a preliminary study with 10 participants. Investigating sensor positions and modalities, we found that a single IMU on the upper arm yielded an accuracy of 80.73% for the assessment of arm movement. We implemented our approach as a smartphone application and found that runners improved their arm movement using our application within a user study including 23 participants. Results from questionnaires revealed high user acceptance (average rating of 8 from 10 possible points).

A Data-Driven Approach to Kinematic Analysis in Running Using Wearable Technology

Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.

Real-time detection and recommendation of thermal spots by sensing collective behaviors in paragliding

Community intelligence is often manifested in distinct collective behavior patterns. We investigate on the exemplary use case of paragliding how real-time participatory mobile sensing can be exploited to infer collective behavior patterns and to conclude about community intelligence. In particular, we present a system to simultaneously aggregate flight information from many paraglider pilots using their location-aware mobile phones. We show that the real-time detection of collective behavior patterns that emerge among the pilots leads to an uncovering of regions with ideal thermal characteristics. Providing an intuitive visualization of this collectively gathered information can help pilots to extend their flight time and to fly longer distances. We perform a series of test flights to assess the technical feasibility as well as the real-time performance and conduct interviews with experienced pilots to evaluate user aspects and incentivization.

Automated assessment of gait deviations in children with cerebral palsy using a sensorized shoe and Active Shape Models

Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.

Exploration of head gesture control for hearing instruments

In this work, we investigated the BENEFIT of head gestures as a user interface to control hearing instruments (HIs). We developed a prototype of a head-gesture-controlled HI, which was based on a customised wireless acceleration sensor for unconstrained and continuous real-time monitoring of the users head movements. We evaluated the system from a technical point of view and achieved a precision of 96% and a recall of 97% for spotting the two head gestures used: tilting the head to the left and right side. We further evaluated the system from the users point of view based on the feedback from 6 hearing-impaired HI users (4 men, 2 women, age 27-60).We compared our head-gesture-based control to existing HI user interfaces: HI-integrated buttons and HI remote control. We found that the benefit of the different HI interaction solutions depends on the users current situations and that all participating HI users would appreciate head gesture control as an additional, complementing user interface.

Quantified performance: assessing runners with sensors

A look at how athletic performance can be measured outside of the laboratory.