1. Introduction
The training squat process aims to adequately strengthen the muscles around the knees and hip joints and fortify the lower back. Also, lifting loads from the ground could be an excellent exercise to activate muscles to be applied in different sports [
1,
2]. The normal range during knee flexion is between 90 and 130 degrees [
3]. However, the risk of getting injured is high in the first few attempts until the athlete learns to perform the move adequately. This mostly occurs in amateur athletes, for whom exercising is not their primary job, who work out several days a week, mainly to be healthy [
4]. Therefore, the common mistakes when learning to perform squats are: (i) starting by folding the knees, which puts pressure on the knee ligaments; (ii) allowing the knees to pass over the feet, since this might put more pressure on the lower back; (iii) the feet are unbalanced, which might affect knees and hips; (iv) allowing the knees to point outwards or inwards, which might affect ankles and hips; and (v) failing to reach at least 90 degrees when the knee is flexed [
5]. As a result, these issues performing squats could be summarized by the following three main mistakes: knee abduction position, knee adduction position, and wrong angle flexion.
Figure 1 shows the right and wrong knee positions for squats. Considering the identified mistakes during squatting, evidence suggests that misalignment in the knee joints can increase the risk of knee injuries, particularly to the ligaments and cartilage. The main health problems in the knee produced as a result of these issues include Anterior Cruciate Ligament (ACL) Injury, Medial Collateral Ligament (MCL) Injury, Meniscus Tear, Patellar Tendinitis, and Patellofemoral Pain Syndrome [
6].
A good knee biomechanical technique during squats is based on the proper support of the feet, which should be separated to shoulder width with the toes facing forward [
7]. Additionally, the knees should follow the direction of the feet and should not exceed the level of the toes’ tips to prevent internal rotation [
8]. Conversely, squats may require weightlifting from the ground to maintain the same ergonomic posture as squats and avoid future illness.
Figure 2 depicts the proper posture during the activity, beginning with separated legs and placing the feet at shoulder level, followed by knee flexion while maintaining a straight back posture (
Figure 2 was purchased by the authors in https://sp.depositphotos.com/114062280/stock-photo-squats-on-his-chest.html (accessed on 25 November 2022) and modified by them to adapt it to the interests of the proposed research). In this scenario, training techniques for muscle conditioning and posture are required in the sports industry, where managers and athletes focus on improving the kinematics and kinetics of athletes [
9]. Therefore, bio-mechanical analysis might also minimize athletes’ risks and possible future illnesses [
10,
11].
High-performance centres for athletes commonly use inertial measurement units (IMUs) to determine the flexion/extension angle of the knee joint, defining it as the angle between the upper and lower leg along the main axis of relative motion [
12,
13]. Furthermore, alternative technologies, such as cameras or Kinect, are helpful tools in sports. Unfortunately, they have specific requirements and traditional gyms do not provide the support needed, such as isolated environments and fixed locations without vibrations. For this reason, wearables are suitable solutions for designing individual scenarios in which several amateur athletes work simultaneously.
Following the trend of presenting individual solutions when people are working out necessitates that wearables make decisions locally to avoid bottlenecks in the communication channel and sharing unnecessary information that the wearable can handle itself. This decentralized computing architecture also provides a secure environment where the information stays safe on the device and specific information can only be shared with another device. Therefore, machine learning (ML) models allow patterns in data to be identified, and with current microcontrollers, the wearable can make inferences locally. Furthermore, each user could have a personal trainer on the device to alert them if their squat movement is incorrect, decreasing the chance of injury.
Based on the factors above, this research aims to develop a smart wearable to classify biomechanic movements when amateur athletes perform squats [
14]. Therefore, the wearable comprises a microcontroller, battery, RF wireless communications, and sensors. The collected data were annotated by personal trainers to train different machine learning (ML) models. Next, the models were tested to determine the most suitable solution based on computational metrics, such as memory consumption and execution time. Finally, the model was exported to the wearable to be tested in real-trial conditions and to check the device’s accuracy. In short, the main contributions of these works are presented as follows:
Present a novel ML workflow running on the device to make inferences locally and prevent injuries in amateur athletes.
A fair comparison between classical ML models and Deep Learning architectures to be exported in wearables.
The rest of the paper is organized as follows:
Section 2 shows the background section and related works.
Section 3 presents the wearable electronic design. The ML workflow is presented in
Section 4. The results are shown in
Section 5. Lastly,
Section 6 shows the conclusions and future works.
Author Contributions
R.P.A.-R.: original draft preparation, formal analysis, and software; V.C.E.-C.: data curation, original draft preparation, and investigation; P.D.R.-M.: methodology, writing—review and editing; G.S.: resources, project administration; supervision; writing—review and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Informed Consent Statement
Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Obuda University (protocol code: OE-DI-205,2023 approved on 28 November 2022).
Data Availability Statement
The data is unavailable due to privacy.
Conflicts of Interest
The authors declare no conflict of interest.
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