*2.6. Prototype*

A small physical design that is easy to place on the body is crucial. The sensor node features a round design with no sharp edges. The final prototype weighs 38 g and has dimensions ø6 cm × 1.5 cm. The structure of the case is shown in Figure 8. The wireless charging coil is positioned at the bottom (1). It is held in place by some offsets in the case (2). On top of that is the battery (3). Above the battery is the PCB (4) which is supported by four pins in the case (5). Everything is fastened nicely by the cover (6), which can be attached with a twist top. We did not ye<sup>t</sup> hermetically seal the case for the initial experiments. By applying some sealant on the twist top, one can make the case more waterproof. Figure 9 shows the assembled prototype of the sensor node. The total cost of components is 28€ with case and 22€ without the case.

**Figure 8.** Cross-section of the sensor node. 1: wireless charging coil, 2: offsets, 3: battery, 4: PCB, 5: support pins, 6: twist top.

**Figure 9.** Result: sensor node in the 3D printed case. (**a**) Bottom side: wireless charging coil and battery. (**b**) Topside: IMU, microcontroller, and BLE chip.

#### **3. Validation with Easily Accessible Equipment**

For the validation of the accuracy of the motion measurements realized by the sensor node, it is common to use professional equipment. The static verification process of the IMU has already been performed by using a computer monitored pan-tilt unit to place the sensor node in specific angles or by using a Vicon motion capturing system [6,8,27]. In the validation of the Madgwick filter for example, a Vicon motion capture system is also used [28]. Sensor validation on this equipment in general yields very accurate results but it is less accessible, expensive and time-consuming.

We propose an alternative, very accessible way of validation using convenient equipment in the context of designing a low-cost system that is user-friendly. With photogrammetry, one can ge<sup>t</sup> a fairly accurate representation of the performance of the sensor node. In this method, we take and interpret photographic images of positions of the sensor. By comparing the data from the IMU with the data extracted from images, the static error on the measurements can be derived. The advantages are that this method can be performed almost anywhere and can be used with consumer off-the-shelf equipment. Since, in contrast to professional cameras, lower-cost equipment, such as a smartphone camera, suffers from lens distortions and lower quality recordings, some measures must be taken. To minimize the effect of the lower quality equipment, the camera is placed horizontally and perpendicular to the wall. This way, foreshortening effects are eliminated. Furthermore, the sensor is positioned such that its projection lies near the center of the image where radial distortion is minimal. This eliminates the need for a camera calibration procedure. Finally, we add several markers to the scene as shown in Figure 10. The relative position of these markers is measured up to ±2 mm.

Since all we need is angles, we can perform measurements in the image and transfer them to the reference system of the sensor node. By attaching a lever to the sensor node, the accuracy of the readings in the image increases. The angle of the sensor can easily be measured by indicating front and endpoints of the lever (red and green points in Figure 10) and mapping these points in the image to points on the wall, using the coordinate system defined by the surrounding markers. By comparing the data from the IMU with the data extracted from the images, the static error on the measurements can be derived for the pitch and roll axis. In our experiments, only static measurements are performed. Dynamic measurements are possible as well, in which case video instead of images should be recorded and the video frames must be synchronized with the output data of the sensor node. Doing so, one can obtain angles at frame level. Instead of manually indicating points in each video frame, this process can be automated using image tracking [29,30].

**Figure 10.** Method for sensor validation based on photogrammetry using convenient, commercial off-the-shelf equipment. By comparing the data from the IMU with the data extracted from images, the static error on the measurements can be derived for the pitch and roll axis.

Table 2 gives an overview of the measurements. For roll and pitch angles, the setup as shown in Figure 10 is used with the sensor node rotated 90◦ between roll and pitch measurements. Since the yaw values have no real fixed orientation, relative measurements are taken by using the setup as shown in Figure 11 where the sensor and markers are positioned on the floor instead of against the wall. Several static measurements were performed. The static sensor drift is 2◦ per half hour. The average error on the pitch axis is 3.06◦, the average error on the roll axis is 2.75◦ and the average error on the yaw axis is 4.04◦.


**Table 2.** Result of pitch, roll, and yaw static measurements with their respective error at different angles.

Alternatively, it is possible to measure all three (roll, pitch, yaw) angles at once by measuring the position of the lever endpoints in 3D using a stereo or multi-camera setup. However, drawbacks of such a method are the much higher complexity, the need for calibration and synchronization, and the lower accuracy in the depth dimension.

There are some irregularities in the measurements. The yaw value at 90◦ seems to be <sup>o</sup>ff. A root cause could be the influence of a nearby magnetic object. The sensor can ge<sup>t</sup> disturbed in the near proximity of magnetic objects such as speakers and smartphones. These magnets create a distortion in the magnetic field which isn't fixed to the reference frame of the sensor node, thus can't be corrected for in calibration. The user can perform reliable measurements when staying half a meter away from

these objects to obtain accurate measurements. The pitch error at 90◦ is also too large. The reason is that Euler angles are not good at representing orientations in the neighborhood of 90◦ [13].

**Figure 11.** Photogrammetry-based method for yaw axis sensor validation.

#### **4. Validation with Real-Life Exercises**

To evaluate and validate the dynamic behavior of the sensor node and real-life operation, two back exercises are performed. The first exercise starts with a person kneeling with hands on the ground. The back is periodically rounded and made hollow, thus demonstrating the periodic concavity of the spine. This is illustrated in Figure 12.

**Figure 12.** Illustration of the first exercise: periodic concavity of the spine (Images provided by Pocket Yoga (www.pocketyoga.com)). The arrows indicate the position of the sensor node. (**a**) Start position. (**b**) End position.Illustration of the first exercise: periodic concavity of the spine

Figure 13 presents the result of the measurements. The exercise has been performed in a set of 3 repetitions. A periodic movement with a variation of ±45◦ on the roll axis can be observed. The pitch axis shows a little bit of sideways rotation in the lower back. The yaw axis is stable, which is to be expected. A second captured exercise is the lateral rotation of the back, illustrated in Figure 14. The patient should rotate the hull sideways, while maintaining stable lower limbs. The measured result is represented in Figure 15. An angular deviation of ±50◦ is present in the yaw axis data. Small changes in roll and pitch values are also observed. These two exercises provide a first evaluation of the dynamic characteristics of the sensor node. We clearly see that the amount of samples taken is appropriate to acquire accurate results. However, more testing, either by dynamic photogrammetry or with specialized equipment, is needed before a firm conclusion on accuracy can be made.

**Figure 13.** Exercise: Rounded back—hollow back. A periodic movement with a variation of ±45◦ on the roll axis can be observed. The pitch and yaw axis are stable.

**Figure 14.** Illustration of the second exercise: lateral rotation of the back (Images provided by Pocket Yoga (www.pocketyoga.com)). The arrow indicates the position of the sensor node.

**Figure 15.** Exercise: Rotation of the back. A periodic movement with a variation of ±50◦ on the yaw axis can be observed. The roll and pitch axis are stable.

#### **5. Opportunities in e-Treatment Applications and Extended Functionalities**

We here first explain the opportunities opened up by stand-alone low-cost and low-complexity sensor nodes in physio-therapeutic e-treatment. We benchmark the current solution and introduce further extensions of the system that can bring interesting features for both private and professional users.

#### *5.1. Opportunities in Supporting e-Treatment in Physiotherapy*

The presented wireless sensor node has been designed to meet the particular needs to support physiotherapy treatment. We wish to introduce technical support at the patient's side to improve both curative and preventive treatment. The sensor thus enables *e-treatment*, which we define as

(remote) physical therapy that is supported by measurements made by wireless sensors. In a curative treatment, the patient can wear the sensor to assist the physiotherapist in the evaluation of (eventual take-home) rehabilitation exercises. A preventive treatment could consist of monitoring a person's daily movements or measuring a patient's flexibility. We specifically expect measurements at work to be interesting, knowing that the large majority of neuromusculoskeletal disorders result from repetitive movements and bad posture at work [31].

Also important in our definition of e-treatment, is the word *remote*. In the case of remote treatment, the patient is not physically present in the physiotherapist's practice, but for example at home and possibly assisted with one or more sensors. Especially because of the increasing cost of healthcare in our ageing society, it is important to look at efficient and low-cost alternatives. The connection is then real-time through a conference call, or non real-time by exchanging exercises over a manual for example. There are several reasons why a remote session can be preferred over a conventional consultation:


The effectiveness of e-treatment in a remote sense is exhaustively discussed in [32]. A last important field of application is the education of physiotherapists. With the help of our technology in a bigger ecosystem, we want to reach physiotherapist with e-learning and help them train and improve. In summary, the sensor can enable remote treatment, as well as support conventional consultations or even acquire measurement data for preventive purposes.

#### *5.2. Extension to Multiple Sensor Nodes*

Richer information and support in rehabilitation and e-treatment could be offered by the combination of multiple sensor nodes, either of the same type or using heterogeneous sensors. An especially relevant type is an Surface Electromyography (sEMG) sensor module for measuring muscle activity. While we have designed the first prototype for this sensor type, in a future version we will combine the IMU and the sEMG sensor into one module. By combining these sensors, we can capture a more complete picture of what the human body is doing. However, this generates extra technological challenges, especially with respect to synchronization, both intra- and inter-module, required to ensure concurrent measurements. Synchronization between the sEMG and the IMU can be implemented using a shared clock. Both sensors will experience the same clock drift. BLE beacon packets from a central node, in this case the receiver, or a custom protocol can be used to synchronize the clocks between sensor nodes [33]. The data can be transmitted using unidirectional beacon packets without re-transmission. This type of data transfer is very simple but does not guarantee the packet arrives at the receiver. A better way would be to use the BLE re-transmission functionality to ensure the packets are received properly. Time synchronization beacon packets could be sent in between. It is evident that both the electrical and the mechanical design will be more complicated, not in the least because of the need to integrate the functions in a small space.

#### **6. Conclusions and Future Work**

**Conclusion.** In this paper, a wireless on-body sensor node for measuring movement is presented. The careful choice of components, software optimizations, and overall low power design considerations lead to a sensor node with an autonomy of 28 h. An 'always-on' buttonless design, with a standby time of 8 months is developed that is ready to measure whenever it is picked up. We explained the calibration of the sensor node and zoomed in, in particular on a photogrammetric procedure to validate the sensor with easily accessible, low-cost equipment. On-device sensor fusion by using a Madgwick filter yields static results of on average 3.28◦ with a drift of 2◦ per half hour. The final prototype weighs 38 g and measures ø6 cm × 1.5 cm. The result of this work can be used in a broad range of applications. It allows doctors and physiotherapists to have an easy to use device to pass along with patients and afterward interpreting the results, it can be used for live monitoring of rehabilitation exercises or anything motion tracking related.

**Future work.** We see multiple opportunities in future work to both the current sensor node, and to extend it with new functionality and features. Firstly, we plan to further examine the accuracy of the sensor node by checking it against specialized equipment. We will add other sensors to ge<sup>t</sup> a more in-depth view of the human body. We also designed a sEMG sensor for measuring muscle activity. These two sensors could be integrated into one module to perform simultaneous measurements. Synchronization, both inter- and intra-sensor node, will be implemented to ensure precise, simultaneous measurements. A future upgrade could also implement a real-time calibration by using artificial intelligence [34]. This could well be implemented on a low power microcontroller with an ARM Cortex M4 chip (nRF52832 from Nordic Semiconductor), which is already used in the BLE module. By running the Bluetooth stack and the peripheral code on the same chip, we could eliminate the central Cortex M0+ microcontroller and further reduce the power consumption. We could also design our own PCB antenna. In the current design, the data is, other than being visualized, not further processed. To detect and analyze complex movements, further data analysis as well as learning algorithms can be implemented. Another extension to the system is a direct communication between the sensor nodes and a smartphone through an app. This eliminates the need for a separate receiver.

**Author Contributions:** Conceptualization, Formal analysis, Investigation, J.C.; Methodology, J.C. and M.V.; Supervision, L.V.d.P.; Writing—review & editing, J.C., L.M., J.V.M., S.G., M.V. and L.V.d.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** NOMADe project of the EU Interreg Program France-Wallonie-Vlaanderen under gran<sup>t</sup> agreemen<sup>t</sup> 4.7.360.

**Acknowledgments:** This research has been supported by the NOMADe project of the EU Interreg Program France-Wallonie-Vlaanderen under gran<sup>t</sup> agreemen<sup>t</sup> 4.7.360. We thank our colleagues in the NOMADe project for the valuable inputs leading to the design of our wireless sensor node that is adequate for usage in physiotherapy treatment.

**Conflicts of Interest:** The authors declare no conflict of interest.
