Motion sensors for knee angle recognition in muscle rehabilitation solutions

Citation: Franco, T.; Sestrem, L.; Henriques, P.R.; Alves, P.; Varanda Pereira, M.J.; Brandão, D.; Leitão, P.; Silva, A. Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions. Sensors 2022, 22, 7605. https://doi.org/10.3390/ s22197605 Academic Editor: Giovanni Andrea Casula Received: 16 September 2022 Accepted: 1 October 2022 Published: 7 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions Tiago Franco 1,* , Leonardo Sestrem 1, Pedro Rangel Henriques 2, Paulo Alves 1, Maria João Varanda Pereira 1, Diego Brandão 3, Paulo Leitão 1and Alfredo Silva 4 1Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal 2ALGORITMI Centre, University of Minho, 4800-058 Braga, Portugal 3Federal Center of Techonology of Rio de Janeiro (CEFET/RJ), Rio de Janeiro 20271-204, Brazil 4INOVA+, 4450-309 Porto, Portugal *Correspondence: [email protected] Abstract: The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O( 1 ) were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor. Keywords: IMU sensor; algorithmic complexity; knee angle; muscle rehabilitation; wearable system 1. Introduction Over human life, our body goes through several muscular and hormonal changes. Generally, a healthy person reaches the peak of their strength and muscle mass between 25 and 34 years old. Afterwards, the human body slows down the metabolism, hormonal cycles, and muscle recovery. The intensification of these symptoms is usually reported at 50 years old and can lead to disorders such as sarcopenia or degenerative diseases such as Knee Osteoarthritis (KOA) [1]. The World Health Organization (WHO) reports [ 2 ] that the recovery and maintenance of functional capacity are one of the main concerns for healthy aging, especially since the worsening of symptoms caused by pathologies related to aging, such as KOA, can compromise human locomotion capacity, requiring the help of an assistant and reducing independence [ 3 ]. In addition, the world’s elderly population is growing fast, with 1 billion elderly people living now, which is 2.5 times greater than in 1980. In the search for new treatments that can better fit the needs of elderly patients who suffer from muscular disabilities, the NanoStim project emerges. The NanoStim project aims to reduce the burden on healthcare services by developing a solution that allows electrostimulation treatment to be performed at the patient’s home. Nowadays, the treatment with electrostimulation for muscle strengthening is divided into sessions Sensors 2022,22, 7605. https://doi.org/10.3390/s22197605 https://www.mdpi.com/journal/sensors Sensors 2022,22, 7605 2 of 19 performed in a physiotherapy clinic, requiring two or three visits per week for a session that lasts 40 min to complete. For this treatment at home to be feasible, the architecture of an electronic computercontrolled system was designed, including a wearable component capable of applying an electrostimulation protocol defined by the physician [ 4 ]. The wearable technology was chosen due to its unique advantages of instantaneity, flexibility, and the ability to transport sensors easily [ 5 ]. Thus, in addition to allowing the treatment to be performed at home, it is also possible to track biophysical and biomechanical signals during a treatment session and use the data acquired to adjust the stimulation protocol considering the particularities of each patient. In order to understand which sensors can bring relevant information to the proposed treatment, a literature review [ 6 ] was conducted looking for studies that used biomechanical data to classify the stages of KOA and raise characteristics that contribute to the pathology interpretation. As a result, two sensors were highlighted as the most significant in identifying distinctions between the motion behavior of patients, the Electromyographic (EMG) sensor and the Inertial Measurement Unit (IMU) sensor. EMG sensors can monitor the electrical activity of a muscle during a given movement or activity through electrodes placed on the surface of the skin. This information can be essential for muscle rehabilitation treatment, providing metrics to adjust stimulation parameters relative to muscle effort during a treatment session. In addition, the use of electromyography can be considered common in physiotherapy clinics, reducing the learning curve of professionals in interpreting the proposed treatment. As part of the NanoStim project, the implementation of a wearable system capable of acquiring EMG signals and performing electrostimulation simultaneously can be found in the article [7]. The knee angle is the most significant parameter in the KOA classification, mainly due to the difference in the behavior of the lower limbs in everyday activities, such as walking. The studies conducted reported that the behavior of patients tends to present a similar pattern depending on the stage of the pathology. In addition to the use of cameras, the most used method to acquire the knee angle was through two IMU sensors. The first sensor is positioned on the patient’s thigh, and the second is on the shin; thus, the angle of each sensor is correlated to calculate the knee angle. Likewise, the knee angle also becomes relevant in our context, expanding our ability to classify the progress of a treatment based on the difference in behavior recorded over the sessions. In addition, with the streaming data, it is possible to verify if the movement performed by the patient during the treatment is correct and to act if it is not. myHealth is a mobile app for Android designed to offer a technological interface between the patient and the physician in an electrostimulation treatment at home. This application under development was able to apply a stimulation protocol and collect data from the EMG sensor simultaneously using the aforementioned wearable system. More details about the myHealth app and the communication with the wearable system during a treatment session can be seen in article [8]. In order to understand the movement performed during a treatment session, recognizing the knee angle, we propose in this article two approaches that are implemented in the myHealth app. The first approach implements two wearable modules to perform the acquisition of the IMU sensor and transmit the data via Bluetooth Low Energy (BLE). The second approach implements knee angle recognition with streaming data in a mobile application. This present article proposes the following contributions: a The development of a low-cost wearable system capable of acquiring data from an IMU sensor; b Identification of the information needed to calculate the knee angle and the construction of an interface to recognize knee movement in a mobile app; c Characterization and comparison of low-cost computational filters to improve the accuracy of motion sensors. Sensors 2022,22, 7605 3 of 19 This document contains six more sections. Section 2discusses related works, presenting similar applications found in the literature; Section 3describes the proposed solution, including the system architecture, the electrical circuit, and the communication protocol. Section 4explores the mathematical model used to calculate the angles from the IMU sensors. Section 5presents the algorithms and tests performed to correct sensor reading errors. Section 6describes the steps followed to implement knee angle recognition in the mobile application called myHealth. Section 7reports the main conclusions and future work. 2. Literature Review Currently, it is possible to find comprehensive literature using motion sensors during rehabilitation sessions. In a literature review [ 9 ] on the topic focused on technological and clinical advances, evidence is reported that indicates a potential benefit for pathologies such as stroke, movement disorders, knee osteoarthritis, and running injuries. Similar to the objective of the NanoStim project, Sultan [ 10 ] carried out a study applying an electrostimulation treatment in patients with KOA through a wearable device and a mobile application. The wearable used was capable of collecting Range Of Motion (ROM) values through two accelerometers. However, the acquired data were not used in the treatment, only stored by the app. After the end of the treatment session, the data were sent to the cloud and made available for the physician to monitor the progress. Although the patient was required to adjust stimulation intensity and treatment duration, the treatment showed an improvement in ROM values and a significant reduction in pain scores. Gait analysis is another way of measuring body movements, body mechanics, and the activity of the muscles. In [ 11 ], Milic employed an evaluation of the gait parameters to understand metabolic and mechanical variables. For this study, a tool called Optogait was used. This tool can display all of the collected data in real-time through a software platform and is paired with lateral and sagittal video analysis. In this study, the Optogait system was positioned on a treadmill where the candidate walked, and it was possible to observe the parameters of the gait cycle in real-time. The system also provides feedback regarding movement asymmetries and what can be employed for clinical intervention. The authors demonstrated that the proposed Iso-Efficiency Speeds (IES) method offers the highest performance benefits while lowering or at least not increasing the metabolic cost. Although this study employs a deterministic analysis due to equations for uphill walking gait, the results are concise and in accordance with the literature’s desired outputs. Machine Learning (ML) techniques are also being explored to improve the diagnosis using data from IMU sensors. Mezghani [ 12 ] creates a dataset from a commercial device capable of recording the knee angle in search of mechanical biomarkers. In this study, it was possible to classify the KOA stages using the Kellgren and Lawrence scale with 85% accuracy. Similarly, Kobsar [ 13 ] also uses IMU sensors to classify KOA using ML. This study proposes the creation of a wearable device and tests different positions for gait identification. Although the study did not specifically use the knee angle, it was able to reach 81.7% in the classification. Despite this, in the systematic review [ 14 ] on the accuracy of clinical applications using wearable motion sensors, it is reported that it is difficult to estimate the reliability of the studies. This is because many of the studies explored use different and sometimes inadequate methods, making the task of correlating the real advances achieved by this technology inaccurate. In addition, the studies found generally do not offer a detailed explanation of how the wearable system works, especially the integration with sensors. In this line, the study reported by Almeida [ 15 ] contributed to our work reported in this article. The authors developed a wearable acquisition system capable of collecting data from the IMU sensor and transporting the data collected via Wi-Fi to a client system developed in Python. The study develops a proof of concept by comparing three correction filters (Complementary, Kalman, and Madgwick) in three different scenarios. The authors made the code and libraries used for the tests performed available on GitHub to make it Sensors 2022,22, 7605 4 of 19 possible to replicate the wearable system. The study points out Madgwick as the filter with a lower error percentage, followed by the Kalman and Complementary filters. Despite this, the authors processed the data in the cloud and did not consider the computational cost of the filtering algorithms and the amount of data required for transmission. This can be a problem in embedded systems, as processing power is generally very low, and transmission technologies such as Bluetooth Low Energy allow for the exchange of small data packets. 3. Wearable Acquisition System The IMU is a type of wearable technology that can be employed to measure motion biomechanics [ 16 ]. The IMU sensor is usually composed of an accelerometer and a gyroscope, both with three axes (x,y,z); it is also possible to find models that include a magnetometer. The metrics collected from accelerometer sensors, such as the magnitude of an acceleration, loading rate, and shock attenuation, are similar to metrics obtained using force plates [ 17 ]. When the gyroscope and/or magnetometer sensors in an IMU are used, the acquired results provide information on the kinematics, including segment and joint rotations [18]. One IMU model commonly employed in wearable applications is the MPU-6050. This device offers low power consumption, low cost, and high-performance requirements for smartphones, tablets, and wearable sensors [ 19 ]. With its ability to precisely and accurately track user motions, the MPU-6050 allows MotionTracking technology to convert handsets and tablets into powerful 3D intelligent devices that can be employed in health monitoring applications [ 20 ]. Furthermore, this IMU device has a three-axis gyroscope, three-axis accelerometer, a Digital Motion Processor TM (DMP), and a dedicated I 2 C sensor bus, all in a small package (4×4×0.9)mm [19]. Regarding the sensor precision, the MPU-6050 features three 16-bit Analog-to-Digital Converters (ADCs) for digitizing the gyroscope outputs and three 16-bit ADCs for digitizing the accelerometer outputs, which allows it to track both fast and slow motions. In addition, the parts feature a user programmable gyroscope full-scale range of ± 250, ± 500, ± 1000, and ± 2000 °/s and a user-programmable accelerometer full-scale range of ± 2 g , ± 4 g , ± 8 g , and ±16g[20]. To take advantage of the functionalities available in the MPU-6050, an architecture based on the ESP32 Microcontroller Unit (MCU) was designed, as illustrated in Figure 1. The proposed module comprises a battery, a DC–DC voltage regulator, MCU, and the IMU sensor. The employed source was a conventional 3.7 V 700 mA h lithium-ion battery. The voltage regulator used was the S09 model due to its low cost and output voltage level, which allows an input voltage of (3–15)V , output voltages of 3.3 V /4.2 V /5.0 V /9.0 V /12.0 V , and maximum output current of 0.6 A . The MCU was selected as the ESP32 due to its low cost and the availability of wireless communication protocols (Wi-Fi and BLE), which provide the I 2 C bus to collect the acquired data from the MPU-6050 and sends the collected data through BLE to a mobile application. From the defined scheme, the electronic components were soldered on a universal perforated board and fixed with hot glue in a case developed in a 3D printer. The case illustrated in Figure 2was designed to protect the electronic components and provide a way to tie the wearable system to a surface, such as a person’s thigh. Thus, to keep the case stable in the desired location, it has made two gaps on each side of the case, making it possible to pass clothing elastics and make a knot. ESP32 is an MCU with enough computational power to acquire sensor data at high frequencies since the clock of a standard model, such as ESP32-WROOM-32D, is above 150 MHz . However, the wearable system under development will not only collect data from the IMU sensor, but the software will also be responsible for performing the following tasks simultaneously: (1) collect data from the EMG sensor, (2) receive the stimulation protocol, (3) apply the stimulation, and (4) transport all collected data via BLE to the mobile app. Sensors 2022,22, 7605 5 of 19 BLE +3.3 V Switch Battery Voltage Regulator (S09 DC-DC) Microcontroller (ESP32) Inertial Measure Unit (MPU-6050) Figure 1. IMU acquisition system diagram. Figure 2. The electronic components soldered inside the case. Considering the scenario described, the wearable systems, capable of performing the electrostimulation sessions, Ref. [ 7 ] were refactored with the addition of IMU data acquisition. To simulate the designed treatment activities, the refactored software was installed in one of the wearable modules. For the second module, new software was implemented and programmed to only acquire the data from the IMU and transmit it via BLE. With this, it is possible to carry out the tests considering the real scenario expected by the software and the hardware, manifesting resource limitations of the computational power. Given this, the data acquisition of the IMU sensor was implemented in a thread programmed to collect a sample every 8 ms , ideally resulting in a sampling frequency of 125 Hz . However, due to the sharing of processing power with the other tasks, the data collected showed acquisitions of 99–100 Hz . Furthermore, the average time interval between each collection was 9.8 ms , with some peaks above 24 ms . The chart in Figure 3 displays the time difference between each sample collected by the wearable system in an example of an acquisition performed. BLE was chosen for the data transmission resource because it presents a lower power consumption than standard Bluetooth and Wi-Fi, significantly increasing battery life. However, in addition to being expensive in terms of processing power, version 4.2 of the BLE limits the amount of data per packet to 517 bytes. As a result, to send all the raw data acquired by the IMU sensor, it is necessary to transmit at least 200 data packets per second to keep the mobile app synchronized. This is because the raw data includes six variables of 16 bytes for the accelerometer and gyroscope and one more variable of 4 bytes for the time interval, resulting in 100 bytes per sample. Sensors 2022,22, 7605 6 of 19 Figure 3. ∆Time of IMU samples collected by the wearable system. Optimally for the proposed solution, it would only be necessary to send the sensor angle in relation to the sagittal plane of the human body. Each processed angle sample requires 4 bytes: 1 byte for the sign, 2 bytes for the integer value, and 1 byte for the mantissa. Thus, it is possible to send up to 125 samples per packet in more than one second of data acquisition. The implemented strategy consisted of sending an IMU data packet every 400 ms , essentially aiming to make the mobile app more synchronized. Each data packet has a vector with three variables of 4 bytes each: the calculation of the roll angle, the value of the X-axis of the gyroscope, and the time interval. Thus, every 400 ms , an average of 40 data samples are generated, resulting in 480 bytes. In this way, it is possible to transmit all the data collected up to that moment, and the maximum would be 43 samples (516 bytes) per packet. To start and finish data collection, the mobile application that can receive the EMG signal during a treatment session [ 8 ] was also refactored to receive the IMU data. Thus, during the session, the application processes and stores the biofeedback data in an internal file. When the session ends, the app sends the file to the cloud via an HTTP API, according to the system architecture [4]. 4. Mathematical Model The Mathematical model used in this work is based on the theory of multibody system dynamics, as presented by Olinski et al. [ 21 ]. In this approach, the orientation of a body in space is given by the orientation of a local frame attached to the body with respect to a reference coordinate system (Figure 4). Figure 4. Sensors with the reference coordinate system. Sensors 2022,22, 7605 7 of 19 Considering the particular case in which the orientation changes occur in a specific plane, as presented in Figure 5a, the mapping of frames with respect to a reference frame can be represented by a single rotation from a reference to another (Figure 5b). Figure 5. ( a ) Frame representation with respect to a reference frame. ( b ) Linear mapping of a frame to another. To map a frame worth in respect to another in the case of rotations in the YZ-plane of an angle Φj around the x-axis, as presented in Figure 5, the Euler angles are determined by applying the linear mapping successively from rotation matrices ( RΦ1 and RΦ2 ) in each of these spaces. The rotation matrix is given by Equation (1), where j=1 or j=2: Rj=  1 0 0 0cos(Φj)−sen(Φj) 0sen(Φj)cos(Φj). (1) Considering the sagittal plane as the plane of reference for the movements, once the abduction/adduction angles are neglected (Figure 6), the orientation of the leg’s frame and the thigh’s frame, both concerning the inertial coordinate system, are represented as rotations in the referred plane. Once the monitored movement happens in the sagittal plane, the knee angle is given by the difference between the thigh and leg angles Φknee =Φ2−Φ1. Figure 6. Sensors with the angles representation. Adapted from [21]. Sensors 2022,22, 7605 8 of 19 Once the IMU’s data are measured with respect to the inertial coordinate system, the attachment of an IMU to each body permits the determination of their orientation naturally with respect to the inertial system, and the difference between them gives the knee angle. 5. Acquisition Optimization As described in Section 3, IMU sensors such as the MPU-6050 model can capture a given movement with reasonable accuracy and sensitivity. However, the performance presented in the real environment shows inconsistency and electrical noise in the acquired samples. Thus, to improve the accuracy of the collected data, two preliminary activities will be described: the calibration of the sensors to remove the offset values and the optimization using a low-cost computational filter that will be implemented in the wearable system. 5.1. Calibration Although the IMU sensors are already calibrated by their manufacturers, over time, it is possible to record measurements that are completely different from zero when the sensor is static, as reported by Woodman et al. [ 22 ]. To align the measurements to zero, the following steps were performed: 1. Position the wearable modules on a straight surface; 2. Turn on the modules and wait for 1 min; 3. Send calibration command from the mobile app; 4. The Wearable modules start acquiring IMU data at the maximum executable frequency for 10 s; 5. The average of the acquired values is calculated for each axis of the accelerometer and gyrocospe; 6. The resulting values are saved in the internal memory of the wearable system. When the wearable system is powered on again, the stored values, known as offset values in our system, will be accessed. Thus, for each sample acquired, the offset value of the respective axis will be subtracted, resulting in an approximate measurement of zero when the module is static. In step two, the modules remain on for one minute before calibration to stabilize the modules and to ensure that the temperature sensor included in the MPU-6050 model does not interfere in the readings. 5.2. Filters with Algorithmic Complexity O(1) Within the Signal Processing area, several filters have been studied to improve the accuracy of IMU sensors. As commented in the literature review, implementing filters such as Kalman and Madgwick can considerably reduce errors in measuring joint angles. However, as described by Valade [ 23 ], implementing filters such as Kalman’s in embedded systems requires a very high computational cost. The Big O notation is one of the most used notations to describe the computational cost of a given algorithm. This notation takes into account the size of the input and counts the number of instructions used to execute a given sequence of code. For example, for an algorithm that calculates whether the given input is even or odd, only one instruction will be used, resulting in an algorithmic complexity of O( 1 ) . For an algorithm that needs to traverse a vector of size n, the algorithmic complexity is O(n) , since at least n instructions will be executed to complete the task [24]. The algorithm complexity presented by Valvede [ 23 ] for the Kalman filter is O( 10 n3) ; for the extended Kalman filter, it is O( 4 n3) . A study on the algorithmic complexity of the Madgwick filter was not found, but as calculations with matrices were used, the algorithmic complexity was to be at least O(n) . Furthermore, these algorithms require memory resources to store the intermediate matrices needed in every calculation. Therefore, three filters with the lowest computational cost with algorithmic complexity of O( 1 ) will be tested: Simple Moving Average (SMA), Exponentially Moving Average (EMA), and Complementary Filter of the accelerometer and gyroscope (CF). The algorithm Sensors 2022,22, 7605 9 of 19 that presents the best results will be implemented in the wearable’s embedded system to run at the time of collection and transmit only the values of the angles to the mobile app. Moving average filters have the ability to smoothen out the oscillations presented in a signal. In this article, the signal is preset by a sequence of numbers ordered by time, also known as a time-series array. To calculate SMA, it is first necessary to define the only required parameter, the window size ( w ) to be moved along the vector. Thus, for each sample acquired, the simple average is calculated among all the last w elements of the array with the new sample included. It is possible to apply the SMA filter by traversing a vector between the defined window to find the new average, resulting in an algorithmic complexity of O(w) . However, to optimize the calculation, a variable was allocated to store the sum of the momentary window. Thus, for each sample it will be necessary to update the sum value and divide by the parameter w to calculate the new SMA. Algorithm 1displays the instructions executed to calculate the SMA. Algorithm 1: Instructions for updating the SMA. Input: signal // signal vector size // size of signal vector w// window size windowSum // sum of the last w-1 signal Output: New simple moving average Function updateSMA: windowSum = windowSum - signal[size-w] + signal[size] return windowSum / w The calculation of the conventional average assumes that all elements have the same weight. The weighted average is the calculation of the average by assigning non-equivalent weights to the elements of the defined window. The EMA filter smoothens the signal by calculating the weighted average considering exponential factor ( α ). The factor α is expressed between 0 and 1 and represents how much the oldest samples should contribute to the result. The EMA filter algorithm can be implemented recursively to optimize processing [ 25 ], requiring only one instruction to perform the calculation, described in Algorithm 2. Algorithm 2: Instructions for updating the EMA. Input: signal // signal vector size // size of signal vector a// factor EMA // calculated EMA vector Output: New exponentially Moving Average Function updateEMA: return a*signal[size] + (1-a) * EMA[size-1] Similar to EMA, the only adjustable parameter to calculate the CF is factor α . However, in this case, the factor α refers to the share of participation between the accelerometer and the gyroscope to compose the angle measured, hence the complementary in the filter name. Complementary filters can be applied whenever there are two or more sources of the same information. For example, Almeida [ 15 ] used the angle calculated through the magnetometer sensor to compose the CF as a third source. From the signal processing point of view, the data acquired by the accelerometer can better measure the slower and static movements, so a low-pass filter is applied. The data acquired by the gyroscope can better measure faster and more dynamic movements, so a Sensors 2022,22, 7605 16 of 19 With the implementations, the wearable modules were put back in the UR3 robot, and the UR3 motion sequence was executed again. This test verified that the rectangles representing the patient’s leg could follow the movement performed by the UR3. The performance remained the same since there were no more changes in the algorithms, only in the graphical part of the app. Test on Volunteers The wearable module systems were applied to a real environment to validate the developed content. Five volunteers were recruited to perform a test performing the knee extension movement. In this test, each volunteer remained seated on a bench and was requested to lift their leg five times for one minute, keeping their leg raised for approximately 4 s each time in the maximum muscle contraction. Figure 14 shows the volunteer’s knee angle being recognized by the MyHealth mobile app through the IMU sensors of the wearable system developed. Figure 14. Wearable system and mobile app recognizing the movement of a volunteer’s leg. As planned, the app successfully recognized the volunteer’s knee angle. However, it was noted that one more adjustment would need to be added to the systems. As can be seen in Figure 14, the wearable module positioned on the volunteer’s thigh does not remain precisely parallel to the thigh bone. Thus, it generates an angular deviation that varies between each person due to the unique anatomy of the leg muscle. Fortunately, this angular deviation tends to be constant and the same during all treatment sessions, varying only by person. Thus, this value can be added and validated by the physician during the first treatment sessions, which are expected to be performed in the clinic and accompanied by the professional. Finally, an example of the information that will be delivered in the administrative portal to the physician after each treatment session can be seen in Figure 15. In this case, the chart shows the volunteer’s knee angle movement during the test performed. For better understanding, the scale was inverted, with 90 º representing the thigh and shin aligned perpendicularly and 0 º when the leg is fully extended. Thus, it becomes more evident after the five times that the volunteer raised his leg, remained elevated, and lowered it repeatedly for approximately 10 s. Sensors 2022,22, 7605 17 of 19 Figure 15. Knee angle acquired by the wearable system. 7. Conclusions This article presents the design, the necessary hardware, and the implementation of a wearable motion acquisition system. This acquisition system is composed of two main components, an ESP32 microcontroller and an MPU-6050 sensor (IMU) and its development is low cost. Due to this, this hardware can present limitations in some environments, as in the case reproduced in this article. The system implemented in the wearable’s hardware was designed to share its processing time to acquire data from an EMG sensor, apply stimulation, and send data to the mobile app. With this in mind, the wearable system was developed with the primary requirement to consume the minimum processing power to collect the data from the IMU sensor. The focus of the system implemented in the wearable is to provide the data necessary for a mobile app to recognize the leg’s movement through the knee angle. According to this objective, three filters were explored to reduce the noise of the IMU sensor data, namely, SMA, EMA, and CF. The CF filter produced the best values among the evaluation metrics, achieving an improvement of 77% in the MSE and 52% in the RMSE in relation to the raw data. The wearable acquisition system showed an absolute average error of 0.6 degrees in recognizing the movement performed by the UR3 robot arm. The recognition of the knee angle will be an asset for the myHealth app, as its objective is to enable performing a treatment with electrostimulation at the patient’s home. For this purpose, an interface was developed to show a sketch of the patient’s leg with its components. As soon as myHealth receives new data from the wearable system, the leg representation is recalculated according to the recognized movement, and a fresh sketch is redrawn, exhibiting the patient’s response to the stimuli. We plan to refactor this interface in the future to show instructions interactively during a treatment session. This way, the treatment may be more attractive to elderly audiences. In addition, all movement performed by the patient during treatment is saved and made available for the physician to analyze later. The system was tested with volunteers in a real environment and successfully measured the movement performed. Due to the heterogeneous anatomy of the volunteers, it was realized that incorporating an initial parameter would be needed. This parameter refers to angular deviation in a static position. This information should be measured and validated by the physician during the first treatment sessions, which are expected to occur in the physical therapy clinic. In addition, the presented system has some limitations and restrictions to operating as planned. The system can measure the angle of only one knee joint at a time, so to measure both knees, it would be necessary to duplicate the IMU sensors and refactor Sensors 2022,22, 7605 18 of 19 the communication between the wearable system and the mobile app. Although the two modules can measure the angle regardless of the surface placed, the calculations performed in the mobile app assume that the modules are in the correct order and positioned on the same axis of the body, with the sagittal or frontal axis being possible. In this way, if the modules have been switched or twisted, the mobile app will misinterpret the measured movement. In the near future, it is expected to embed the wearable modules presented in this article on a Printed Circuit Board (PCB). The PCB designed for the NanoStim project includes an EMG sensor and an electrostimulation actuator. With this in mind, the IMU sensors will be incorporated into a wearable, such as pants or shorts, and, through two cables connected to the PCB, responsible for power supply and data communication. Thus, it is expected to solve the positioning constraint, as the sensors will be positioned and fixed on the wearable in the correct and unalterable order. With the complete hardware, it will be possible to test all the components designed to carry out a treatment session simultaneously and synchronized. As an example of a more complex validation, it can be the use of the Ariel Performance Analysis System (APAS), which uses cameras to measure the movement of the joints, providing a more reliable benchmark for the presented system. Furthermore, we plan to create algorithms that can benefit from the combination of IMU and EMG sensors. An example is implementing a contraction clipping algorithm, using the knee angle to indicate the initial and final moment that the EMG sensor data should be clipped. With the EMG sensor alone, identifying contraction can be tricky since injured or diseased muscles can present low activity in the EMG signal. Author Contributions: Formal analysis, D.B.; Investigation, T.F. and L.S.; Project administration, P.L. and A.S.; Supervision, P.R.H., P.A. and M.J.V.P. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Portugal 2020 in the framework of the NanoStim (POCI-01-0247-FEDER-045908) project, and Fundação para a Ciência e a Tecnologia under Projects UIDB/05757/2020, UIDB/00319/2020, and PhD grant 2020.05704.BD. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Polytechnic Institute of Bragança (protocol code 77/2022 approved on 9 March 2022). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References 1. Cruz-Jentoft, A.J.; Sayer, A.A. Sarcopenia. Lancet 2019,393, 2636–2646. [CrossRef] 2. World Health Organization. Decade of Healthy Ageing: Baseline Report; World Health Organization: Geneva, Switzerland, 2020. 3. Ambrose, A.; Paul, G.; Hausdorff, J. Risk factors for falls among older adults: A review of the literature. Maturitas 2013 ,75, 51–61. [CrossRef] 4. Franco, T.; Henriques, P.R.; Alves, P.; Varanda Pereira, M.J.; Pedrosa, T.; Silva, F.; Leitão, P.; Oliveira, L. 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