May 2024

The trainer in your shoes – sensors for objectivizing screening tests in performance in sports


State-of-the-art foot sensors and automated testing algorithms enable sports professionals to provide evidence-based support for injured athletes through competition preparation. As these innovations transition from science to practice, they must offer flexible use, high data quality, and good usability.

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The sensor opportunity for athlete assessments

Wearable sensors for biomechanical movement analysis pose professionals in competitive sports the opportunity to provide evidence-based support to all athletes throughout their careers (1). Sensors can help sports physicians, sports physiotherapists, and athletic trainers fully exploit performance potential, reduce injury risks, and minimize complications during rehabilitation.

This potential has also been addressed by the VBG, the German Employer’s Liability Insurance. Since July 1, 2022, the renewed bonus program has been in effect, financially rewarding sports entities with professional athletes that conduct “systematic monitoring of physical condition states” to help “identify risk factors for injuries” (2). Special emphasis is placed on sensor technology, as “technical advancements offer excellent opportunities to record athlete stress levels through tracking systems,” according to the guidelines.

According to the VBG sports report, the lower extremities are more frequently affected by injuries, accounting for 55.5% of cases, with ankle (18.2%) and knee (13.5%) injuries being the most common (3). Such injuries often significantly impact athletes’ careers. Therefore, staying injury-free is not just an abstract goal but increasingly pursued systematically by coaches across all performance levels.

While the benefits of capturing biomechanical parameters in professional sports are clear, what about the financially more limited junior and amateur sports sectors? Professional measurement technology often requires investments in the five- to six-figure range. What system properties must be met to overcome existing reservations?

Measurement method overview

In this context, we discuss a new, mobile method for conducting and analyzing biomechanical measurements, using commonly accepted functional tests and performance tests on the lower extremities (Fig-1). The measurement system is based on wireless sensor insoles that capture load distribution on the foot, movements in all directions, and timing (Tab-1). Using pattern recognition, parameters for range of motion, force symmetry, reactivity, and coordination are determined.

Fig-1: Moticon ReGo sensor insoles and app required for data acquisition of sports performance tests and functional tests.

Practically, this means that quantitative parameters such as jump distances, duration, or repetition count, which were mainly collected manually as of to date, are now automatically calculated. In addition, new quantitative parameters such as ground contact times or force values are generated. Most importantly, the system provides a new dimension of quantifiable parameters, like the variance of pressure distribution upon landing or gait line. The test portfolio includes standardized walking, running, hop, and jump tests, as well as proprioceptive skills tests. Besides the sensor insoles, only a mobile phone or tablet is required for installing the application app.

This technology brings the vision of a biomechanics lab in the shoe for everyone a step closer. A broad user base across all performance levels gains access to cost-efficient and location-independent analyses previously reserved almost exclusively for professional sports.

Relevance for competitive sports

For the actual users, this is good news, as functional tests and performance tests combined with biomechanical measurement technology contribute significantly to three areas. First, functional limitations in the lower limbs after injuries can be reliably identified, allowing individualized rehabilitation (4, 5). Second, initial baseline tests and continuous monitoring of athletes bear the potential to unlock performance gains (6). Third, tests can be integrated as pre-injury screenings (PRE) to identify injury risks early and counteract them with individual interventions (7).

Experts agree on the importance of this holistic approach. “Prevention, and especially secondary prevention, play an increasingly important role in elite sports. After an injury, athletes and clubs naturally want the fastest possible return to competition. As a physician, providing the greatest possible safety against re-injury requires objective functional tests, ideally with comparative data from the pre-injury state,” states Dr. Christoph Lukas, a sports orthopedist and team physician to a German premier league basketball team, advocating for more evidence in athlete care.

Although the trend towards sport-specific training forms continues to gain momentum in professional sports, standardized basic tests remain crucial for strength and conditioning training, for athletic training and the return-to-competition cycle. This is also evident from a survey among coaches, therapists, and sports physicians (n=34) from various performance levels and sports in Germany, Austria, Sweden, and the USA. Respondents rated the relevance of 39 sports motor tests for their practice with athletes (Fig-2). Two insights stand out. First, 62.9% of respondents rated the 20 most relevant tests as “very important” or “important,” indicating high relevance. Second, considering that 12 tests are alternative tests with the same purpose (e.g., vertical explosive strength), the number of different tests a measurement system should cover could likely be reduced to 8-10. Important basic tests, such as gait tests, were not part of the survey as they can be used in clinical follow-up independently of the sport.

Fig-2: Results of a survey unveiling most important sports performance tests and functional tests.

Jump and hop tests, in particular, render great importance. They cover a broad spectrum of endpoints for many sports, from stability metrics during landing to explosive strength and reactivity.

Below, we present two showcases from different phases of athlete care that illustrate the potential applications of the measurement method within testing.

Frist goal: regain walking skills

After sports injuries to the lower extremities, the initial focus is on restoring proper walking. The first showcase highlights how gait tests combined with biofeedback can be used in this phase. The basic outcome parameters are available for running and sprint tests as well.

Case study: hip head dislocation

A young female track and field athlete (12 years old) suffered a slipped capital femoral epiphysis (SCFE), right, which was surgically corrected through repositioning and screw fixation (Fig-3). From July 23 to October 11, 2022, 21 therapy-accompanying gait tests were conducted with the patient.

Fig-3: X-ray images of the epiphysiolysis capitis femoris before and after fixation.

Partial load training with biofeedback

The partial load regime was medically prescribed for eight weeks, gradually increasing from complete relief to 20 kg and 30 kg partial load with forearm crutches to full load. For the first 15 tests, the integrated biofeedback function using sonification and visual display was used to help the patient adhere to the partial load (Fig-4). Additionally, the pressure distribution can be displayed. This method is similarly used in several clinical studies for gait training interventions, including a clinical trial at the Stanford University, USA (10).

Fig-4: Visual biofeedback options for partial weight bearing training.

Standardized reports are available for analyzing load patterns. The load histogram from Test 1 clearly shows that the 20 kg partial load on the right was maintained, as no loads above the threshold were measured (Fig-5). The pressure distribution (Mean Pressure Distribution) indicates a noticeable pressure peak in the right forefoot from the time of full load at Test 18, which normalizes by Test 21. From full load onwards, the force curves (Ground Reaction Force) take the form of a dynamic gait pattern with two maxima for heel strike and forefoot push-off, with asymmetry decreasing until Test 21.

Fig-5: Development of typical load-related gait characteristics during the regait training process.

Coordination and quantitative parameters

The analyses provide insights into motor control and coordination. The mean gait line (Gait Line) reveals the foot roll-over process in anterior-posterior and transverse directions (Fig-6). In Test 1, a shortened gait line in the forefoot and rearfoot is visible. This form is explainable given the partial load, where the foot was placed cautiously and centrally. Functional movement aspects gain importance only under full load, allowing targeted interventions through physiotherapy in case of abnormalities. Additionally, the dispersion of initial and final ground contact points significantly decreases, indicating that gait becomes more stable towards the end of rehabilitation. Several other parameters provide insights into patients’ performance, such as the number of steps and stride length.

Fig-6: Development of typical coordinative and mobility based gait characteristics during the regait training process.

Trend analysis for progress control

The app also features a function to display any result parameter over time across multiple tests, enabling individual progress monitoring. The example shows how the partial load to full load on the right leg and the symmetry index (LSI) develop over time (Fig-7).


Continuous testing and prevention

Continuous monitoring of performance-related movement parameters through functional tests and performance tests allows training content to be individually tailored to each athlete. The second case example shows selected screening data from a first Bundesliga soccer team (21 athletes), collected using a previous version of the measurement method. The test battery included jumps for heading with subsequent landing.

Identifying intra- and interindividual differences

Athlete 1 underwent baseline screening (BS) on 16.07.2016 and follow-up screening (FS) on 17.03.2017. In the BS, the athlete showed a significant valgus collapse of the knee joint during the landing phase (Fig-8), a risk factor for anterior cruciate ligament injuries (11). This also manifested in a pronounced medial pressure peak in the forefoot. The athletic trainer intervened with a specific stabilization training program over the following months, significantly reducing the collapse by FS.

Fig-8: Pre and post training intervention examples of professional soccer players with pronounced valgus collapse (left) and varus collapse (right).

All three athletes showed similar jump heights in the BS. However, the leg position and pressure distribution during the landing phase vary significantly inter-individually. While Athlete 2 shifts the body’s vertical axis and load distribution to the right in a pronounced varus position, Athlete 3 lands with an almost perfectly stable vertical leg axis and a significantly more homogeneous pressure distribution. Therefore, even within well-managed professional teams, there are considerable, sometimes critical, intraindividual differences regarding functional movement aspects.

Drop jump as a standardized test for reactive strength

The Drop Jump (Fig-9) can be utilized as a standardized test within the aforementioned scenario to assess performance-related parameters such as ground contact times, take-off speed, jump height, or jump forces. So far, photoelectric sensors and force plates have been employed for this purpose in competitive sports. The new measurement method, however, enables the collection of these performance-related parameters regardless of location.

Fig-9: Drop jump test sequence and 2 exemplary outcome sets, representing primary outcome (reactive strength index RSI) and secondary outcomes (force, load distribution, COP variablity).

Additionally, values related to the variance and direction of pressure distribution (Sway Area) as well as the load distribution provide insights into landing quality. Previously, this was only possible with significant effort in laboratory setups. For instance, if, as in the example above for Athlete 1, the medial pressure peak needs to be reduced for preventive reasons, this can be accurately quantified through the percentage load distribution. If a coach or therapist subsequently introduces preventive training measures, the results can be efficiently verified using repetitive tests. Fig-9 shows two example evaluations from an athlete who underwent a training program aimed at improving reactivity.

A fine line: between quality and usability

With regards to the acceptance of body-worn sensors in sports, a fundamental conflict of objectives becomes apparent, distinguishing it from scientific applications.

Conflict between applicability and data quality

On one hand, performance and professional sports in particular demand for high data quality. This is due to the fact that small differences in outcome metrics need to be reliably determined. For example, in healthy athletes, a difference of only 5% between the left and right leg is usually tolerated when measuring jump strength.

On the other hand, complex scientific expert systems stand no chance in practice. Just the initial setup often requires 15-20 minutes. This is “the total time available per athlete for a complete screening in professional soccer with a team size of 20-30 players,” says Professor Thomas Stöggl from the Department of Sport and Movement Science at the University of Salzburg, Austria. As the scientific director at the Red Bull Athlete Performance Center, he also has direct insight into training practices: “In professional sports, we need to square the circle, as we fundamentally benefit from high-quality objective measurements that support rehabilitation or help improve athletes’ performance. However, the measurement technology must not interfere with athletes’ freedom of movement and should be very flexible and time-saving to use.”

Quality criteria for measurement technology

For measurement accuracy, the basic principle is that the measurement error must be smaller than the differences in the target metric being assessed. Various factors influence the results of in-shoe measurement systems: temperature drifts, footwear, or foot shape. Therefore, the validity of the measurement data should always be verifiable through independent scientific publications (8). For highly dynamic tests, a measurement frequency of at least 200 Hz is necessary to capture the maximum values with high probability. For a ground contact phase of 0.15 seconds during a sprint, this results in 28 to 30 data points.

The design of the presented sensor insoles differs little from a regular insert without sensors. They are completely wireless and fully flexible in the forefoot and rearfoot areas, minimizing the bias on athletes. A calibration function automatically compensates for temperature drifts, and a self-test function continuously checks the condition of the sensors, ensuring consistently high data quality.

Standardization aspects as a basis for comparability

Another crucial aspect regarding the monitoring of athletes and patients is the comparability of test results. First, the movement tasks must be standardized. For the same test, different execution forms can be found in both literature and practice. For instance, a drop jump is commonly performed either with free-swinging arms or with hands on the hips (9), making outcomes not directly comparable. Secondly, the retest reliability of the measurement system for real-world use must be scientifically proven. Sensor aging and varying conditions at the deployment site are factors to consider.

Product specifications for superior assessment quality

The aspects shown in Tab-1 highlight system specifications which adhere to the goals of comparability of the outcomes, data quality and usability.

Instruction videos
Step-by-step guide
Clearly illustrated instructions for each test aiming at standardizing motion tasks (e.g. hands on hips during jumping).
Motion context recognitionTest algorithms recognize the context of the movements, allowing failed attempts to be excluded from the test result calculations (e.g. jumping up on a drop jump, ground touch with free leg during hop tests).
Patient reported outcomes and commentsPatient Reported Outcome Metrics (PROMs) can be entered to capture pain level or fatigue or to make additional comments.
Test sequencesCreation of test sequences consisting of multiple tests which are prompted subsequently to make testing sessions more time efficient.
Remote testingTests can be conducted by athletes or patients themselves at any location. Stakeholders, such as trainers, physicians, have real-time access to all test results to promote exchange of information.
Auto calibrationSensor drifts and individual calibration are prompted automatically when required to ensure highest data quality standards.
Tab-1: ReGo system specifications enhancing comparability of outcomes, data quality and usability.


1. Ryan, T., et al. 2016. Wearable Performance Devices in Sports Medicine. SPH. 8, 1: 74-78 

2. VBG (2021). Prämienkatalog ab 2020 – Sport unternehmen mit bezahlten Sportlerinnen und Sportlern für das Prämienverfahren der VBG. Hamburg: VBG

3. Klein, C., et al. 2021. VBG-Sportreport 2021 – Analyse des Verletzungsgeschehens in den zwei höchsten Ligen der Männer: Basketball, Eishockey, Fußball, Handball. Hamburg: VBG

4. Keller, M., et al. 2016. Zurück zum Pre Injury nach Verletzungen der unteren Extremitäten – eine Einteilung funktioneller Assessments. Man. Ther. 20: 16-18

5. VBG 2015. Return-to-Competition – Testmanual zur Beurteilung der Spielfähigkeit nach Ruptur des vorderen Kreuzbands. Hamburg: VBG

6. Jon, E., et al. 2021. The Biomechanical Basis of Training. London: Routledge

7. VBG 2015.. Präventivdiagnostik für den bezahlten Sport – Testmanual zur Präventivdiagnostik im Rahmen des VBG Prämienverfahrens. Hamburg: VBG 

8. Cramer, LA, et at. 2022. Validity and reliability of the insole3 instrumented shoe insole for ground reaction force measurement during walking and running. Sensors 22, 2203

9. Laffaye, G., et al. 2006. Upper-limb motion and the drop jump: Effect of expertise. J. Sp. Med. Phys. Fit. 46, 2: 238-247

10. He, J., et al. 2022. Is remote active feedback gait retraining comparable to in-person retraining 2 years post anterior cruciate ligament reconstruction? Ost. Cart., 30, 1: S153

11. Larwa, J., et al. 2021. Stiff Landings, Core Stability, and Dynamic Knee Valgus: A Systematic Review on Documented Anterior Cruciate Ligament Ruptures in Male and Female Athletes. Int. J. Environ. Res. Public Health 18(7), 3826

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