In the field of conservative scoliosis therapy, therapeutically guided physiotherapeutic scoliosis-specific exercises (PSSE) are supplemented by training units that patients perform independently at home. One of the 7 major schools in the field of PSSE is Schroth therapy, which has established itself worldwide and especially in Germany. Developed in 1920 by Katharina Schroth, this therapeutic method follows five principles: (1) auto-elongation (detorsion), (2) deflection, (3) derotation, (4) rotational angular breathing (RAB), and (5) stabilization. On the basis of diagnostic findings, Schroth also divides the human body conceptually into a 3- or 4-block model (Appendix Chapter 1, Multimedia Appendix 1), which then serves as the basis for the therapy plan. The change in shape of these blocks from a rectangle to a trapezoid and the horizontal positioning visualize the scoliotic trunk deformation and lateral displacement [1].

Another special feature of Schroth therapy compared to other PSSE is RAB. This uses the connection between the ribs and spine and the increase in volume of the chest during breathing to correct the concave and convex areas of the torso through targeted contraction of the torso muscles. This means that every breath can also be a corrective exercise [1]. Some of the frequently used exercises in Schroth therapy, which involve RAB, are “Between Two Poles” (Zwischen zwei Stäben), “50X” (50X Pezziball), and “Sail” (Segel) [1,2]. Further information on these exercises can be found in Multimedia Appendix 2.

These exercises require patients to be able to understand the complex movements cognitively and motorically. This often requires intensive therapy assistance from a therapist or physician, which is not available in the home setting [3]. The lack of safety when training independently and the lack of suitable therapy equipment therefore often lead to reduced motivation, which in turn has a negative effect on the success of the therapy. Various possible solutions are described in the literature: Portable textile systems such as the “Wearable Sensor Array” by Caviedes et al [4], the “Scoliosis Rehabilitation Assistant” by Li et al [5], the “SpineCurer” by Mai et al [6], or the “Wearable Scoliosis Monitoring System” [7] use stretch or inertial sensors placed on the back to track movement. Pattern recognition and linked apps can then be used to monitor scoliosis exercises and provide training instructions [4-6]. Stationary training systems such as the “DISC4SPINE-System” [8,9] or the “rehabilitation exercise system” enable exercises in a seated position [10] or while standing and monitoring of therapy progress using various sensors [8,9]. In addition, several apps and web-based systems are presented, such as “ScolioTrack,” “ScolioMeter,” “ScolioDetector,” “Scoliosis Tracker,” or the “Scoliosis Manager,” which determine the progression of spinal curvature via smartphone, remind patients of appointments, or assist in the management of scoliosis rehabilitation [11,12].

All these wearables, training devices, and apps offer approaches and partial solutions for improving and supporting home-based scoliosis therapy. However, the current literature has not yet identified a holistic system that enables a wide range of exercises and guides and monitors them and, at the same time, increases motivation for training.

In a previous study [13], we investigated the potential for optimizing home-based scoliosis therapy by conducting online surveys of patients with scoliosis and therapists. The results showed that, according to their own statements, patients exercise significantly less at home than what their therapists recommend. The reasons for this are a lack of motivation and uncertainty when performing complex exercises, a lack of awareness of the importance of training, and the absence of supervision by therapists [14,15].

The patients surveyed stated that their motivation could be increased by training together with friends and acquaintances, by a supportive therapy system, and by a digital profile. We also asked about the popularity of various training devices. It turned out that balance boards, wall bars, mirrors, and exercise sticks are among the most popular devices, but that patients use them relatively rarely at home (≤55.6%). Regarding digital tools, patients attached great importance to an app, video support, and vibration feedback. Therapists saw particular advantages in increasing motivation, improving home-based therapy, monitoring therapy progress, and communicating therapy instructions [13].

On the basis of the results of our survey and the lack of devices on the market, the aim of this study was to develop a novel concept for a holistic and digitally assisted scoliosis training system. Consequently, this study focuses on the technical verification of the demonstrator, serving as a prerequisite for future clinical evaluation and formal medical device development. To this end, we have formulated the following hypotheses:

This study was designed as a proof-of-concept and technical verification study. It describes the development process and the resulting demonstrator, its functionalities using exemplary Schroth exercises, and the technical verification of its measurement functions. The study’s findings correspond to Technological Readiness Level 4.

As part of the study, no human participants or animals were involved, and no clinical trials were conducted. Images showing people together with the demonstrator are for illustrative purposes only. All devices containing electrical components were deactivated at the time.

After consultation with the Ethics Commission in Leipzig (Germany), and in accordance with section 15 of the professional code of conduct of the Saxony State Medical Association, no ethics approval was required for the work described.

In the first phase of the development process, we identified requirements for a training system with digital assistance based on the wishes and challenges associated with home-based scoliosis therapy [13]. To address the lack of therapeutic support, low motivation, and limited space, the system should have the following features: a digital profile, exercise instructions, measurement functions to monitor exercise execution, low space requirements, modifiability for holistic training, and the integration of established but rarely used at-home training devices such as balance boards, wall bars, mirrors, and exercise sticks. We have transferred these requirements into a modular approach, which combines conventional devices with sensor functions, and further specified them (Table 1).

For each of these modules, the names, designs, and sensor concepts were created in a second development phase. The first module was named “Wearable-Module” due to its design as a portable, functional textile that is primarily used for tracking. Modules 2 and 3 represent the training components, with the second module named “Bars-Module” because of its rod-shaped design and the third module named “Balance-Module” due to its resemblance to conventional balance boards. The fourth module is used to visualize all sensor data and acts as an interface between the user and the new system, which is why it was named “Interface-Module.” For the entire system, the acronym “iScoolio” was chosen, which combines the terms “scoliosis” and “school” and describes the process of “I school myself,” as the guiding principle of home-based scoliosis therapy.

In the final development phase, we implemented a demonstrator for each module, which is presented as the “iScoolio concept” in the Results section. A key component of this work was the development of a custom circuit board (iScoolio board). The board consists of a microcontroller (BlueNRG-355MT; STMicroelectronics N.V.), inertial sensors (BHI260AB and BMM150; Bosch), and a Wheatstone quarter bridge (R=10 kΩ), and it serves as a basis for all sensor functions. Powered by a lithium battery (LIR2032; AccuCell) and housed in an enclosure (70×34×17 mm), each circuit board can be used as a stand-alone unit. For wireless communication, each board has its own ID and radio channel and transmits its data every 6 milliseconds via its own 2.4 GHz frequency channel. This interval was chosen as a compromise between transmission speed and power consumption. The corresponding receiver scans each channel for 13 milliseconds to ensure data acquisition in case the first transmission fails. This resulted in a sampling frequency of 4 Hz.

A theoretical use case evaluation was conducted using the developed concept for the exemplary Schroth exercises “Between Two Poles,” “50X,” and “Sail” (Multimedia Appendix 2). Of particular interest were the variability, usability, and tracking capabilities of the system.

This paper introduces a concept for a home-based scoliosis training system, presented as a demonstrator and verified technically. The concept builds on findings from a previous online survey on scoliosis therapy, with a particular focus on independent training at home. Consisting of 4 modules, it enables varied training in a space-saving design, including sensor-based monitoring of exercise execution and the output of instructions without the need for additional conventional training devices. Through dedicated test scenarios and test benches, we verified the implemented measurement functions. The results confirm that the sensor framework yields valid and accurate measurements compared to technical references, in line with H₀. The development can therefore be classified at Technological Readiness Level 4. A comparison with other approaches to scoliosis therapy found in the literature is presented in Table 3. The following sections discuss the selected subcomponents of the iScoolio concept.

Motion tracking is one of our core components for monitoring exercise execution. To achieve this, we have integrated 9 inertial sensors into a textile wearable, which also allow for monitoring of posture. During the technical verification of our motion tracking, we examined 3 representative samples of the 9 iScoolio boards. Compared to the optical reference system (ARAMIS 3D), this resulted in an average absolute deviation of 2.29° (SD 1.66°), supporting H₁. It should be noted that the significant outliers in the reference system contributed to an increase in the deviation. These are most likely due to delays or errors in manual image capture. Similar deviations between IMU in wearable applications and an optical system (OptiTrack) were found, for example, by Li et al [16] with 2.00° (SD 2.65°), Teufl et al [17] with ≤2.40°, and Bell et al [18] with ≤3.00°. In terms of repeatability, the iScoolio boards achieved good results with a maximum SD of 1.48°. Studies by Fennema et al [19], Hamidon et al [20], and Beange et al [21] also show that IMUs can exhibit high repeatability and be suitable for motion tracking concepts. We have thus created a basis for monitoring scoliosis exercises.

Unlike the wearables for home-based scoliosis training described in the literature, we have integrated breathing tracking into our system. In many Schroth exercises, the number of breaths taken is used as a counting variable for repetitions. Common measurement systems for respiration that can also be worn on the body include inertial sensors [22,23], stretch sensors [23-26], and bioimpedance sensors [23,27]. Due to their good integrability, we opted for textile strain sensors. Although this measurement principle is susceptible to motion artifacts [26], it is nevertheless very well suited to our application due to the classic sequence of many Schroth exercises: (1) adopting a corrective posture and (2) targeted breathing.

For the technical verification of the sensor concept, we examined 2 respiratory parameters relevant to scoliosis therapy: respiratory rate and respiratory depth. Both can be extracted from the raw data measured using the strain sensors we integrated. This enabled us to verify H₂. However, automated evaluation requires the integration of additional data processing. Low-pass filters and finite impulse response filters, as well as window size [28], maximum-minimum, zero-crossing, and threshold detection algorithms [29], can be used to detect respiratory rate. For the detection of breathing depth, it is sufficient to differentiate between the measured values, as the increasing filling volume of the lungs is accompanied by an increasing expansion of the sensor.

By supplementing motion tracking and posture monitoring with components for measuring respiratory parameters, we have created a technical basis for recording RAB. Further software development and the integration of machine learning approaches are required for accurate tracking.

To provide the most versatile workout possible, we have combined the features of wall bars and exercise sticks into a single training device, the Bars-Module. The supporting feet and handles of this module can be used to measure forces in the upper limbs. The technical verification showed that the demonstrator’s raw sensor signals responded clearly and proportionally to applied forces, which substantiates H₃. On the basis of the 3 selected force levels, nonlinear behavior was observed when considering the entire measuring range, as well as drift behavior. These characteristics, supplemented by the uninvestigated hysteresis, reflect the underlying resistive measuring principle [30,31]. However, these negative effects can be significantly reduced through the use of appropriate algorithms [31]. In addition to the software options, optimization through hardware adaptation would also be conceivable. Due to the available installation space, the force-sensing resistor sensors in the supporting feet could be replaced by high-quality load cells, which in turn would mean a significantly higher financial outlay.

Thanks to the integrated measurement functions of the Bars-Module, there are various options for monitoring the exercise execution. In addition to traditional grip strength, which correlates with scoliosis [32], it is possible to measure isometric force during an exercise. In combination with the posture tracking feature of the Wearable-Module, it is possible to determine whether the user maintains their corrective posture throughout a set. Furthermore, the ability to distinguish between the left and right arms allows for the determination of symmetries or asymmetries in force distribution. In combination with the other modules, this enables an analysis of how the user assumes their corrective posture.

To further increase the variety of exercises, we have added a second training device to our concept. We chose a balance board, which plays an important role in many PSSE exercises. The basis for this is the impairment of the cortical centers of the brain in scoliosis, which are responsible for controlling balance [2]. In their study involving 195 participants (adolescent idiopathic scoliosis [AIS]: n=114, 58.5%; control: n=81, 41.5%), Le Berre et al were able to show that dynamic proprioception is selectively altered in AIS [33]. Targeted training of balance reactions is therefore intended to improve axial, static, and dynamic balance, which in turn helps to intensify self-correction during everyday activities [2]. As an extension of the training functions of conventional balance boards, we have also equipped our module with measuring functions. Integrated sensors can be used to determine both the inclination of the board and the pressure distribution on the standing surface. This makes it possible to monitor the execution of training instructions. We have also created the hardware basis for investigating parameters such as foot pressure distribution and COP, both of which are influenced by scoliosis [34-37]. In their study, Horng et al found a statistically significant linear relationship between the Cobb angle and maximum foot pressure on the hallux [35]. Regarding COP, the study by Larni et al showed a decline in COP parameters in adolescents with AIS compared to the healthy control group in tests using a Kistler force plate [37]. In addition to these standing measurement applications, balance boards equipped with sensors also offer various application possibilities when sitting. Jung et al were able to use integrated acceleration sensors to measure the asymmetrical sitting postures caused by scoliosis and pelvic deformity in patients with AIS [38]. To record all these parameters in detail, further development steps are necessary for our system.

Regarding the detection of stance width, our measurements showed slight absolute deviations from the mean, with a maximum of 1.2 cm. Comparing this value with the intervals described in the literature for targeted changes in the training effect, such as a narrow stance of 31.7 (SD 2.7) cm and a wide stance of 59.8 (SD 5.1) cm [39], it can be seen that these variations are sufficiently small and therefore have no significant negative effect on the measurements.

The verification of our COP measurement function showed that 48% of the measurements had a deviation of ≤1 box and 82% had a deviation of ≤2 boxes. With a sensor element resolution of 10 mm, 82% of the measured values had a COP shift of ≤20 mm. It should also be noted that a single sensor element has a diameter of 6 mm. With a center-to-center distance of 10 mm between these sensor elements, this results in horizontal and vertical dead zones of 4 mm and diagonal dead zones of 8.14 mm. Depending on the positioning of the test weights, this may have resulted in real pressure differences between the 2 clusters, which would also cause a real shift in the COP. The increased edge errors may be attributed to material transitions, higher conductor resistances, or calibration limitations.

These measurement functions allow the user a specific stance width to be set and tracked, which affects the difficulty level of the balance exercises. In addition, the COP can be measured during static standing or targeted balance training. Finally, during seated exercises in which the Balance-Module is used as a support surface, it is possible to measure how hip displacement and seat pressure distribution are used during the exercise.

The Interface-Module completes our system with a digital human-technology interface. On the one hand, it serves to communicate, bundle, and process all sensor data. On the other hand, it contains an exercise catalog and outputs visualizations of the measurement data and therapy instructions via an associated web application. In the targeted therapeutic setting, it functions as a scoliosis-specific software tool, which is represented in the literature with various application scenarios.

Compared to the scoliosis software solutions described by Bottino et al [12], the Interface-Module focuses more on the processing and visualization of sensor data as a basis for exercise monitoring and guidance. Nevertheless, there are minor similarities in terms of the selection of suitable exercises or the provision of information for compliance.

In its current state of development, the iScoolio concept still has some room for improvement. One of the biggest limitations is that motion tracking is currently limited to the torso and head and that all sensor data are recorded at a frequency of only 4 Hz. Furthermore, all iScoolio boards are independent components, which therefore require a separate charging and calibration process. This results in a time-consuming setup process. In addition, exercises in the supine position are not possible due to the size of the current boards. For the training modules, the aim should be to reduce the size of the Balance-Module. In addition, different force sensors, a faster extension mechanism with less backlash, and a locking function for the supporting feet would be useful for the Bars-Module. All these questions will be addressed in further work.

There were also some limitations regarding technical verification. These included the fact that all measurements were limited to repetitions of a specific Schroth exercise.

For the motion tracking investigation, we limited ourselves to 3 representative iScoolio boards. In addition, only points and angles on the frontal and sagittal planes were examined; mixed movements were not. Nevertheless, these data enable us to draw conclusions about the accuracy of the measurement system, which was the aim of the investigation. Another limitation regarding motion tracking resulted from the manual triggering of the ARAMIS 3D system.

Regarding respiration tracking, the test was likewise limited to a single representative sensor. In addition, the filling and deflation speed of the air cushions was limited by the valve installed in the test setup. Furthermore, the rising and falling of the chest was simulated by 2 air cushions, which only produced a selective increase in volume rather than a full-surface increase. Finally, it should be noted that no classic reference measurement system was used for this measurement, but rather an actuator-based approach with high repeatability.

When verifying the handpieces of the Bars-Module, the greatest limitations were caused by the 3D-printed half-shells. These only represent a hand in a highly simplified form and had inaccuracies due to additive manufacturing. Furthermore, the handles were verified with a round wooden profile dummy instead of the metal rod. Both limitations led to a deviation from the real load but represented a suitable approach for integrating a testing machine with regard to the measurement function to be tested.

In the case of the Balance-Module, the limiting factors during verification were, on the one hand, the test weights, which did not accurately represent a human foot in terms of their shape and weight. However, the smaller outline of the weights enabled a more detailed examination of the sensor surface.

Another limitation is that verification was only carried out in a technical setting and without the involvement of human participants. Therefore, no clinical conclusions can be drawn regarding the impact on the effects of scoliosis, such as pain, reduced quality of life, or depression [40,41].

In this paper, we have presented the iScoolio concept, which has the potential to support home-based scoliosis therapy through adaptable devices and tracking elements. Integrated sensors enable monitoring of movements, breathing, forces, and pressure distribution during training. An associated web application visualizes the recorded data and serves as a guide for performing the exercises. Using the new approach could increase motivation, reduce concerns about incorrect exercise execution, and enable adherence monitoring and teletherapy. Technical verification indicates that the integrated tracking methods are functional; however, there is still room for optimization in terms of hardware and software. The system’s greatest areas for improvement include the low sampling frequency (4 Hz), the lack of motion tracking for arms and legs, the long setup process, and measurement inaccuracies in some sensor designs. Future work will address these limitations. Before conducting clinical trials under real-world conditions, further validation is required.