Monitoring of training performance and physical activity has become indispensable these days for athletes. Wireless technologies have started to be widely used in the monitoring of muscle activation, in the sport performance of athletes, and in the examination of training efficiency. The monitorability of performance simultaneously in the process of training is especially a necessity for athletes at the beginner level to carry out healthy training in sports like weightlifting and bodybuilding. For this purpose, a new system consisting of 4 channel wireless wearable SEMG circuit and analysis software has been proposed to detect dynamic muscle contractions and to be used in real-time training performance monitoring and analysis. The analysis software, the Haar wavelet filter with threshold cutting, can provide performance analysis by using the methods of moving RMS and %MVC. The validity of the data obtained from the system was investigated and compared with a biomedical system. In this comparison, 90.95% ± 3.35 for left biceps brachii (BB) and 90.75% ± 3.75 for right BB were obtained. The output of the power and %MVC analysis of the system was tested during the training of the participants at the gym, and the training efficiency was measured as 96.87% ± 2.74.
In recent years, the monitoring of athlete performance has become indispensable for the health of athletes. Wireless technologies have started to be widely used in order to obtain data for the purpose of examining training efficiency in the monitoring of muscle activation and sport performance of athletes [
Traits of SEMG signals obtained during training (frequency, severity, etc.) change depending on the muscle group measured and the severity of contraction [
The most reliable method used in the adequacy and examination of muscle activation in physiological studies is the amplitude analysis carried out on SEMG signals, known as MVC (maximum voluntary contraction) normalization [
The simultaneous monitorability of athlete performance during the process of training is a must for athletes at the beginner level to being able to carry out healthy training in sports like weightlifting and bodybuilding [ Be able to provide the required SEMG data necessary for monitoring training efficiency in performance analysis Be able to filter the noise of movement during isotonic exercises and noise and distortions in SEMG signals appearing as a result of other factors Its procedures like calibration, etc., have to continue for a short time The data obtained have to be at a close accuracy to biomedical systems Has to be simultaneously usable in a training environment
For use in the industrial field, various systems are available for SEMG data collection and processing. To investigate these, WB-EMG [
Comparison of the SEMG acquisition systems.
System | Signal type | Number of channels | Gain | ADC resolution (bits) | Wearable | Filter type | Contraction detection | Real-time MVC norm. | CMRR | Connection type |
---|---|---|---|---|---|---|---|---|---|---|
Proposed system | SEMG | 4 | 4400 | 12 | Yes | Hardware + software | Yes | Yes | >90 | Bluetooth |
WB-EMG | SEMG | 1 | 100–10000 | 12 | No | No | No | No | >90 | Bluetooth |
Biometrics datalog | SEMG | 8 | 1000 | 14 | No | No | No | No | >90 | Bluetooth |
Myo armband | SEMG | 8 | ≥1000 | 8 | Yes | Notch | No | No | >90 | Bluetooth |
Delsys Trignio | SEMG | 16 | 909 | 16 | No | Notch | No | No | >90 | RF |
BITalino | SEMG | Up to 6 | 1000 | 6–10 | Yes | No | No | No | >90 | Bluetooth |
Mbody3 | SEMG | Up to 6 | ≥1000 | 24 | Yes | Hardware + software | No | No | >90 | Bluetooth |
Mpower | SEMG | 4 | ≥1000 | — | Yes | Hardware + software | No | No | >90 | Bluetooth |
MyoTrac | SEMG | 2 | ≥1000 | 14 | Yes | Butterworth | No | No | >90 | Bluetooth |
MyoWare | SEMG | 1 | ≥1000 | — | Yes | No | No | No | >90 | Bluetooth |
Shimmer | SEMG | Up to 60 | ≥1000 | 16 | Yes | Hardware + software | No | No | >90 | Bluetooth |
|
SEMG | 8 | 1–10000 | 24 | No | Hardware + software | Yes | No | >90 | Usb |
When the table is analysed, it is seen that all of these systems can simultaneously observe biopotential changes in muscle or muscle groups monitored during training, but none of them include real-time MVC normalization and contraction detection procedures for performance analysis during training.
That these features can be monitored simultaneously during the training process may be useful especially for beginner athletes to perform a healthy training in sports like weightlifting and bodybuilding, for the performance evaluation of the athlete until the motor skills of the movement are improved and at necessary moments in preventing the injury process by intervening in training.
Based on these elements, a new wireless wearable SEMG data collection system has been introduced which enables performance monitoring and analysis at training time with its real-time MVC normalization and contraction detection processes. The SEMG circuit used in our system is designed by us to be used in future studies and to be developed according to our needs.
In the presented system, digital filtering is also used in addition to hardware filtering in SEMG circuit. These numerical filters are Haar wavelet filters with Threshold cutting based on (TCHW) and linear Kalman [
Isotonic contraction encompasses exercises where muscle tendons get shortened to generate movement. Any kind of movement, ranging from weightlifting to rowing and running, is in this category [
The SEMG circuit design details are given below. The circuit consisting of 4 channels could monitor the biopotential change of 4 different muscle groups at the same time. So, it is possible to monitor biopotential changes occurring in muscles in symmetrical movements that affect multiple muscle groups (e.g., the Bench Press movement affects pectoralis major and triceps muscles). The circuit has in each channel, respectively, one instrumentation amplifier, a inverting amplifier, a low-pass filter, a high-pass filter, and a full-wave rectifier. The circuit has a diode for input protection, a pointer indicating that the circuit is working, and a start-up button. During working, the LD1117 regulator was used for the Bluetooth feed and the 7805 regulator for the +5 volt and −5 volt op-amp feed (Figure
Block diagram and mounted state of the SEMG circuit. (a) Regulator circuit. (b) Instrumentation amplifier. (c) Inverting amplifier. (d) 1st-order HPF. (e) 2nd-order Sallen–Key LPF. (f) Full-wave rectifier. (g) PIC 16F1786. (h) Bluetooth module. (i) Mounted state of the SEMG circuit.
As stated in [
In SEMG applications, analogue (hardware) and digital (software) filters are used to remove unwanted component noises and process the necessary parts in the SEMG signal [
In the circuit, analogue filtering is performed by low- and high-pass filters. Ideal SEMG signals are observed between 50 Hz and 500 Hz and should be filtered from frequency components outside this range [
Then, the whole SEMG signal was moved to the positive level using the full-wave rectifier (Figure
The Pic16F1786 microcontroller with connected full-wave rectifier outputs contains 11 12 bit A/D (Analogue/Digital) converters. The data obtained from the rectifier of each channel in the circuit are connected, respectively, to the RA0-RA3 inputs of this controller. This microcontroller performs the A/D conversion in 20 ms time intervals through the program we write. The converted channel data are turned into a string, and this sends data from the RC0 output to the Bluetooth module (Figure
Five males and two females voluntarily participated in our study and have at least 2 years of experience in strength training. The information of the participants is shown in Table
Information about age, gender, weight, and height of the subjects.
Participant no. | Age | Gender | Weight (kg) | Height (cm) |
---|---|---|---|---|
1 | 21 | Male | 80 | 163 |
2 | 25 | Male | 82.3 | 178 |
3 | 29 | Male | 87 | 180 |
4 | 33 | Male | 85 | 177 |
5 | 37 | Male | 104.6 | 193 |
6 | 24 | Female | 70 | 180 |
7 | 27 | Female | 68 | 172 |
The participants were informed about the content of our study, and a signed consent form was obtained from all of them. All exercises and measurements were made under the supervision of a specialized trainer. As described in the recommendations of the European initiative known as SENIAM (surface electromyography for noninvasive muscle evaluation of muscles) by selecting 10 mm diameter electrodes shown in Figure
Example view of electrodes and shielded cables.
Our experiments consist of 3 parts. In the first part, 8 repetitions and 1 set of alternate dumbbell curl (ADBC) training was performed using a maximum load of 60–70%. In this section, firstly, it is investigated whether the analogue filter data obtained from the circuit in the training reflect the biopotential activity changes that occur during the training. In the sequel, the analogue filter data obtained from the circuit are processed by means of Kalman and threshold cut Haar wavelet filter (TCHW) to eliminate noise sources and to investigate the perceptibility of the isotonic contractions.
In the second part, the accuracy of the developed system was compared with the biomedical system (Viking on Nicolet EDX) used in Karaman State Hospital (See Table
In the third part, the availability of moving RMS and %MVC values as the screen output of the system was investigated in terms of performance feedback. For this purpose, first, the moving RMS values obtained by asking users to perform a second ADBC (8 repetitions 1 set) training were recorded. In addition, a %MCV measurement was made by asking all users in the training environment to lift 5 kg dumbbell and maximum weight (Men 17.5 kg, 20 kg, and 25 kg dumbbell; women 12.5 kg and 15 kg dumbbell) they can.
Kalman filter is used to estimate the system status from input and output information with the previous information of a model in a dynamic system indicated by the state-space model [
Here,
In (
In HW, the main wavelet acts as the wavelet transform but is scaled and shifted during this procedure of wavelet transform [
HW is a wavelet-based, scaled, “square-shaped” array of functions.
The Haar function
Since the SEMG signals are user-based, SEMG signals between isotonic muscle contractions may vary according to the individual. In the method we use with HW, the individual waits for approximately 2–4 seconds with the weight in his hand before starting training and in the meantime, the procedure of threshold cutting in the system can be carried out. The threshold cutting is based on the calculation of the average value (
Here,
After the SEMG signal is captured, the commonly used RMS or MA values are analysed by using [
Another method we use as MA is the technique of analysing changes in a data set to estimate long-term trends. For a given N time window, if the values
Thus, changes in the time window given at the
So, it can be measured how much power is obtained from the muscle through the moving RMS value.
The MVC (maximum voluntary contraction-maximum amplitude of the signal) normalization is widely used in SEMG signals as an amplitude analysis technique. The results are shown as a percentage (%MVC) of the MVC value that can be used to create a common background when comparing data between subjects [
Thus, it can be scaled how much power is obtained from the muscle or muscle groups investigated in repetitions in each set of training.
Our system has the ability to follow the biopotential changes of four different superficial muscle groups at the same time. The reason why the system is designed with 4 channels is that most movements used in bodybuilding and weight training activate at least 1 or 3 muscle groups at the same time. The system takes the biopotential signals of the muscles that are activated during training through surface electrodes (Figure
Overview of the system. (a) Connecting electrodes before training (Photoshoot by Orucu). (b) Block diagram of the SEMG circuit. (c) Block diagram of the analysis software. (d) User interface of the analysis software.
The analogue-filtered data of the first 4 repetitions of ADBC training performed by participant number two is shown in Figure
Sample analogue filtered data obtained from the SEMG circuit during training: (a) Sample results of participant number two, (b) sample results of participant number six.
The left BB (LBB-Left Biceps Brachii) data are obtained from CH1 (first channel of the SEMG circuit), and the right BB (RBB-Right Biceps Brachii) data are obtained from CH2 (the second channel of the SEMG circuit). From the data obtained, some decrease in Rep2b, Rep3a, Rep3b, and Rep4a (between 100 and 200
Other data of training performed by participant number four are shown in Figure
Data of other participants obtained from these trainings. (a) Results of participant number one. (b) Results of participant number three. (c) Results of participant number five. (d) Results of participant number six. (e) Results of participant number seven.
In Figure
Comparison of the filtering results. (a) SEMG data without the filter. (b) Premeasurement for threshold filter. (c) SEMG signal with threshold + HW filter. (d) SEMG signal with Kalman filter.
The accuracy of the data obtained from our system was compared through the data belonging to two men and two women with the SEMG device in Karaman State Hospital (Figure
(a) A measurement taken in the hospital environment and a photograph of the current biomedical system. (b) A photograph taken at the gym before training.
As shown in Table
Moving RMS Results in Gym and Hospital. Note that “M” denotes the measurement number; “BB” denotes biceps brachii; “S” denotes system; “H” denotes hospital, “MN” denotes muscle name.
Participants/weight (no./kg) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
M | MN | Type | 1/idle | 2/idle | 3/idle | 4/idle | 1/5 | 2/5 | 3/5 | 4/5 | 1/25 | 2/25 | 3/15 | 4/12.5 |
I | Left BB | S | 70.69 | 69.72 | 51.18 | 43.82 | 123.69 | 129.54 | 97.54 | 93.64 | 914.7 | 935.98 | 566.98 | 547.64 |
H | 67.13 | 72.31 | 47.24 | 45.9 | 137.42 | 141.94 | 108.66 | 101.05 | 950.94 | 1112.53 | 616.53 | 604.36 | ||
Right BB | S | 71.4 | 69.75 | 49.66 | 42.45 | 119.11 | 127.41 | 96.86 | 93.95 | 960.71 | 937.69 | 565.69 | 515.43 | |
H | 69.64 | 70.51 | 50.22 | 43.93 | 135.57 | 143.13 | 107.93 | 97.14 | 943.82 | 1117.15 | 615.15 | 545.64 | ||
|
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II | Left BB | S | 69.86 | 68.84 | 52.03 | 43.15 | 121.82 | 128.9 | 95.71 | 93.8 | 907.35 | 934.5 | 563.5 | 518.06 |
H | 70.39 | 69.61 | 51.76 | 43.75 | 138.87 | 139.69 | 105.78 | 101.55 | 942.14 | 1116.89 | 614.89 | 595.59 | ||
Right BB | S | 69.84 | 71.34 | 49.01 | 46.68 | 122.96 | 126.95 | 96.5 | 93.61 | 950.6 | 932.61 | 562.61 | 526.48 | |
H | 71.82 | 67.83 | 50.25 | 43.27 | 136.52 | 142.8 | 106.73 | 102.46 | 1002.4 | 1110.94 | 612.94 | 598.81 | ||
|
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III | Left BB | S | 69.45 | 70.89 | 50.96 | 46.22 | 124.61 | 128.91 | 97.88 | 89.07 | 907.15 | 934.34 | 564.34 | 511.19 |
H | 68.57 | 69.97 | 50.24 | 46.71 | 138.81 | 139.69 | 106.74 | 105.67 | 1000.8 | 1115.11 | 614.11 | 583.55 | ||
Right BB | S | 71.64 | 67.65 | 52.01 | 43.55 | 122.97 | 129.74 | 95.38 | 92.52 | 948.63 | 930.36 | 562.36 | 539.49 | |
H | 68.56 | 69.63 | 49.72 | 44.79 | 135.94 | 145.8 | 107.5 | 104.28 | 1000.9 | 1110.61 | 612.61 | 543.35 |
In the system designed as a result of this measurement, accuracies of 90.95% ± 3.35 for the left BB and 90.75% ± 3.75 for the right BB were obtained.
During the training, the volunteers were asked to perform a second training in order to obtain the moving RMS values given back to the user as feedback. The results are presented in Figure
ADBC results of participants.
Moving RMS results in gym as training feedback.
Muscles and participants | Rep1 | Rep2 | Rep3 | Rep4 | Rep5 | Rep6 | Rep7 | Rep8 |
---|---|---|---|---|---|---|---|---|
LBB 1 | 862 | 798 | 738 | 683 | 782 | 556 | 715 | 741 |
LBB 2 | 845 | 779 | 852 | 786 | 590 | 812 | 796 | 766 |
LBB 3 | 757 | 725 | 721 | 560 | 712 | 699 | 645 | 736 |
LBB 4 | 810 | 841 | 840 | 804 | 828 | 832 | 791 | 830 |
LBB 5 | 704 | 802 | 651 | 670 | 604 | 354 | 558 | 701 |
LBB 6 | 387 | 413 | 395 | 354 | 367 | 403 | 381 | 370 |
LBB 7 | 316 | 328 | 372 | 346 | 377 | 302 | 328 | 319 |
RBB 1 | 876 | 833 | 811 | 790 | 815 | 846 | 704 | 653 |
RBB 2 | 823 | 817 | 847 | 834 | 649 | 747 | 621 | 770 |
RBB 3 | 821 | 793 | 766 | 696 | 566 | 884 | 685 | 785 |
RBB 4 | 832 | 853 | 856 | 821 | 819 | 808 | 809 | 815 |
RBB 5 | 815 | 763 | 750 | 753 | 718 | 707 | 725 | 714 |
RBB 6 | 389 | 422 | 418 | 350 | 371 | 402 | 361 | 378 |
RBB 7 | 331 | 380 | 365 | 351 | 372 | 348 | 314 | 341 |
Thus, it can be seen that the system can achieve minimum and maximum values of biopotential changes in muscles during training as in [
Finally, the users were asked to lift 5 kg of dumbbell and the maximum weight they could lift. Thus, the %MVC was measured to be used in performance feedback through the obtained moving RMS values. The results obtained are presented in Table
%MVC results in gym.
Muscle name | Part. no. | kg | SMVC ( |
MVC ( |
%MVC |
---|---|---|---|---|---|
LBB | 1 | 5 | 138.3 | 850.26 | 16.26 |
17.5 | 845.47 | 850.26 | 99.43 | ||
2 | 5 | 138.30 | 992.6 | 13.93 | |
25 | 987.15 | 992.6 | 99.45 | ||
3 | 5 | 152.6 | 960.13 | 15,89 | |
20 | 898.25 | 960.13 | 93,55 | ||
4 | 5 | 155.4 | 963.30 | 16.13 | |
20 | 929.1 | 963.30 | 96.44 | ||
5 | 5 | 158.9 | 967.5 | 16.42 | |
20 | 956.76 | 967.5 | 98.88 | ||
6 | 5 | 89.5 | 615.7 | 14.53 | |
15 | 614.74 | 615.7 | 99.84 | ||
7 | 5 | 93.5 | 545.2 | 17.14 | |
12.5 | 538.9 | 545.2 | 98.84 | ||
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RBB | 1 | 5 | 136.5 | 875.9 | 15.58 |
17.5 | 854.15 | 875.9 | 97.51 | ||
2 | 5 | 140.22 | 996.4 | 14.07 | |
25 | 985.92 | 996.4 | 98.94 | ||
3 | 5 | 154.42 | 972.4 | 15.88 | |
20 | 893.56 | 972.4 | 91.89 | ||
4 | 5 | 156.8 | 968.7 | 16.18 | |
20 | 930.76 | 968.7 | 96.08 | ||
5 | 5 | 164.45 | 972.65 | 16.90 | |
20 | 910.5 | 972.65 | 93.61 | ||
6 | 5 | 97.1 | 614.4 | 15.80 | |
15 | 609.9 | 614.4 | 99.26 | ||
7 | 5 | 92.2 | 515.16 | 17.89 | |
12.5 | 506.3 | 515.16 | 98.28 |
If Table
When data obtained from the designed SEMG system are compared with data obtained from the systems used in the biomedical field, it is seen that it has 90.85% accuracy. As digitally filtered data are compared, it is seen that TCHW method produces better results compared to Kalman filter. TCHW can soften data as processable and can also completely filter out unwanted signals between muscle contractions. It also eliminates the distortions in data expressed as artifact. Kalman filter appears to soften the data but not to be able to completely filter the signal between muscle contractions. Moreover, it is seen that the system can scale the strength obtained as moving RMS during the training on the basis of %MVC with the success rate of 96.87% ± 2.74 in terms of efficiency. This allows the data obtained to be used in the simultaneous performance monitoring and analysis of athletes.
Thanks to this system, it is thought that athletes will be able to examine their performances instantly for each training and make their training more efficient. It is possible to create intelligent training corners by using the system in gyms. It is thought that the system can easily be used by athletes, trainers, kinesiologists, and rehabilitation experts in bodybuilding trainings and rehabilitation processes. It is possible to improve system features by increasing the number of channels, further reducing the PCB size and adding extra sensor. It can be possible to follow more complicated movements (deadlift, barbell row, etc.) by increasing the number of channels. By making the size of system smaller, it can be possible to place it into textile product. In addition, by adding the pulse oximetry sensor to the system, oxygen consumption can be observed during the training. In our future studies, it is being thought of supporting the system with an image processing system in order to determine movement distortions in addition to use it for monitoring training performance and efficiency.
The data used to support the findings of this study are included within the article.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to thank Assistant Professor Dr. Yusuf Er (Karamanoğlu Mehmetbey University Physical Education and Sports Teaching-Recreation Management) for his helpful advice on various technical issues and Atilla Sönmezışık (Antalya Sport Center) for his training support.