Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participants
2.3. Instruments and Exercise Equipment
2.4. Procedure
2.5. 1RM Prediction
2.6. Data Analysis
3. Results
3.1. Exercise Recognition and Repetition Count
3.2. 1RM Predictions
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Pernek, I.; Hummel, K.A.; Kokol, P. Exercise repetition detection for resistance training based on smartphones. Pers. Ubiquitous Comput. 2012, 17, 771–782. [Google Scholar] [CrossRef]
- Bleser, G.; Steffen, D.; Reiss, A.; Weber, M.; Hendeby, G.; Fradet, L. Personalized Physical Activity Monitoring Using Wearable Sensors. In Smart Health: Open Problems and Future Challenges; Holzinger, A., Röcker, C., Ziefle, M., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 99–124. [Google Scholar] [CrossRef]
- Pernek, I.; Kurillo, G.; Stiglic, G.; Bajcsy, R. Recognizing the intensity of strength training exercises with wearable sensors. J. Biomed. Inform. 2015, 58, 145–155. [Google Scholar] [CrossRef] [Green Version]
- Shoaib, M.; Bosch, S.; Incel, O.D.; Scholten, J.; Havinga, P.J.M. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors 2016, 16, 426. [Google Scholar] [CrossRef]
- Guo, X.; Liu, J.; Chen, Y. Automatic personal fitness assistance through wearable mobile devices: Poster. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, Association for Computing Machinery, New York City, NY, USA, 16–20 October 2016; pp. 437–438. [Google Scholar]
- Guo, X.; Liu, J.; Chen, Y. When your wearables become your fitness mate. Smart Health 2020, 16, 100114. [Google Scholar] [CrossRef]
- McBurnie, A.J.; Allen, K.P.; Garry, M.; Martin, M.; Thomas, D.; Jones, P.A.; Comfort, P.; McMahon, J.J. The Benefits and Limitations of Predicting One Repetition Maximum Using the Load-Velocity Relationship. Strength Cond. J. 2019, 41, 28–40. [Google Scholar] [CrossRef]
- Fleck, S.J.; Kraemer, W. Designing Resistance Training Programs, 4th ed.; Human Kinetics: Champaign, IL, USA, 2014. [Google Scholar]
- McMaster, D.; Gill, N.; Cronin, J.; McGuigan, M. A Brief Review of Strength and Ballistic Assessment Methodologies in Sport. Sports Med. 2014, 44, 603–623. [Google Scholar] [CrossRef]
- Brzycki, M. A Practical Approach to Strength Training; Masters Press: Grand Rapids, MI, USA, 1989. [Google Scholar]
- Epley, B. Poundage Chart, Boyd Epley Workout; Body Enterprises: Lincoln, NE, USA, 1985; p. 86. [Google Scholar]
- JGonzález-Badillo, J.; Yañez-García, J.M.; Mora-Custodio, R.; Rodríguez-Rosell, D. Velocity loss as a variable for monitoring resistance exercise. Int. J. Sports Med. 2017, 38, 217–225. [Google Scholar]
- Jidovtseff, B.; Harris, N.K.; Crielaard, J.-M.; Cronin, J.B. Using the load-velocity relationship for 1RM prediction. J. Strength Cond. Res. 2011, 25, 267–270. [Google Scholar] [CrossRef]
- Jovanović, M.; Flanagan, E.P. Researched applications of velocity based strength training. J. Aust. Strength Cond. 2014, 22, 58–69. [Google Scholar]
- Sayers, M.G.L.; Schlaeppi, M.; Hitz, M.; Lorenzetti, S. The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship. BMC Sports Sci. Med. Rehabil. 2018, 10, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Lorenzetti, S.; Lamparter, T.; Lüthy, F. Validity and reliability of simple measurement device to assess the velocity of the barbell during squats. BMC Res. Notes 2017, 10, 707. [Google Scholar] [CrossRef] [Green Version]
- Barrajón, J.P.; Juan, A.F.S. Validity and Reliability of a Smartphone Accelerometer for Measuring Lift Velocity in Bench-Press Exercises. Sustainability 2020, 12, 2312. [Google Scholar] [CrossRef] [Green Version]
- Lorenzetti, S.; Huber, D. Tracking of strength training: Validation of a motion-recognition algorithm & a pilot towards 1RM, muscle loading and fatigue index using a smartwatch app. ISBS Proc. Arch. 2018, 86, 886–889. [Google Scholar]
- Huber, D. Validation of a Motion-Recognition Algorithm Using a Smartwatch. Master’s Thesis, ETH Zurich, Zurich, Switzerland, 2017. [Google Scholar]
- Reynolds, J.M.; Gordon, T.J.; Robergs, R.A. Prediction of one repetition maximum strength from multiple repetition maximum testing and anthropometry. J. Strength Cond. Res. 2006, 20, 584–592. [Google Scholar] [CrossRef] [PubMed]
- Shimano, T.; Kraemer, W.J.; Spiering, B.A.; Volek, J.S.; Hatfield, D.L.; Silvestre, R.; Vingren, J.L.; Fragala, M.S.; Maresh, C.M.; Fleck, S.J.; et al. Relationship between the number of repetitions and selected percentages of one repetition maximum in free weight exercises in trained and untrained men. J. Strength Cond. Res. 2006, 20, 819–823. [Google Scholar] [CrossRef] [PubMed]
- Walker, O. Velocity Based Training, Science for Sport, 5 August 2017. Available online: https://www.scienceforsport.com/velocity-based-training (accessed on 27 August 2021).
- Carroll, K.M.; Sato, K.; Bazyler, C.D.; Triplett, N.T.; Stone, M.H. Increases in Variation of Barbell Kinematics Are Observed with Increasing Intensity in a Graded Back Squat Test. Sports 2017, 5, 51. [Google Scholar] [CrossRef]
- Helms, E.R.; Storey, A.; Cross, M.R.; Brown, S.R.; Lenetsky, S.; Ramsay, H.; Dillen, C.; Zourdos, M.C. RPE and velocity relationships for the back squat, bench press, and deadlift in powerlifters. J. Strength Cond. Res. 2017, 31, 292–297. [Google Scholar] [CrossRef]
- Zourdos, M.C.; Klemp, A.; Dolan, C.; Quiles, J.M.; Schau, K.A.; Jo, E.; Helms, E.; Esgro, B.; Duncan, S.; Merino, S.G.; et al. Novel Resistance Training–Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve. J. Strength Cond. Res. 2016, 30, 267–275. [Google Scholar] [CrossRef] [Green Version]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Elsevier Science: Burlington, NJ, USA, 2013. [Google Scholar]
- Conceição, F.; Fernandes, J.; Lewis, M.G.C.; Gonzaléz-Badillo, J.J.; Reyes, P.J. Movement velocity as a measure of exercise intensity in three lower limb exercises. J. Sports Sci. 2015, 34, 1099–1106. [Google Scholar] [CrossRef] [Green Version]
- Sánchez-Medina, L.; González-Badillo, J.; Perez, C.; Pallarés, J. Velocity-and power-load relationships of the bench pull vs. bench press exercises. Int. J. Sports Med. 2014, 35, 209–216. [Google Scholar] [CrossRef]
- Ormsbee, M.J.; Carzoli, J.P.; Klemp, A.; Allman, B.R.; Zourdos, M.C.; Kim, J.-S.; Panton, L.B. Efficacy of the Repetitions in Reserve-Based Rating of Perceived Exertion for the Bench Press in Experienced and Novice Benchers. J. Strength Cond. Res. 2019, 33, 337–345. [Google Scholar] [CrossRef]
- Pallarés, J.G.; Sánchez-Medina, L.; Pérez, C.E.; DE LA Cruz-Sanchez, E.; Mora-Rodríguez, R. Imposing a pause between the eccentric and concentric phases increases the reliability of isoinertial strength assessments. J. Sports Sci. 2014, 32, 1165–1175. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Tian, Y.; He, M. Monocular human pose estimation: A survey of deep learning-based methods. Comput. Vis. Image Underst. 2020, 192, 102897. [Google Scholar] [CrossRef]
- Balsalobre-Fernández, C.; Marchante, D.; Baz-Valle, E.; Alonso-Molero, I.; Jiménez, S.L.; Muñóz-López, M. Analysis of wearable and smartphone-based technologies for the measurement of barbell velocity in different resistance training exercises. Front. Physiol. 2017, 8, 649. [Google Scholar] [CrossRef] [Green Version]
- Balsalobre-Fernández, C.; Marchante, D.; Muñoz-López, M.; Jiménez, S.L. Validity and reliability of a novel iPhone app for the measurement of barbell velocity and 1RM on the bench-press exercise. J. Sports Sci. 2017, 36, 64–70. [Google Scholar] [CrossRef]
- von Marcard, T.; Henschel, R.; Black, M.J.; Rosenhahn, B.; Pons-Moll, G. Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 614–631. [Google Scholar] [CrossRef]
Variable | Total (n = 30) | Men (n = 14) | Women (n = 16) |
---|---|---|---|
Age [years] | 28.4 ± 6.0 | 28.1 ± 6.5 | 28.6 ± 5.8 |
Height [cm] | 1.72 ± 0.09 | 1.79 ± 0.06 | 1.67 ± 0.07 |
Weight [kg] | 72.3 ± 15.7 | 84.6 ± 13.2 | 61.9 ± 8.1 |
BMI | 24.1 ± 3.4 | 26.5 ± 3.3 | 22.0 ± 1.8 |
Years of training experience | 4.8 ± 3.9 | 6.8 ± 4.8 | 3.1 ± 1.7 |
1RMBBP [kg] | 70.6 ± 34.5 | 96.5 ± 36.9 | 43.9 ± 7.6 |
1RMBBS [kg] | 116.0 ± 42.2 | 143.2 ± 50.6 | 84.8 ± 19.9 |
1RMBDL [kg] | 126.5 ± 47.7 | 154.3 ± 61.1 | 94.3 ± 20.4 |
Abbreviation | Ref | Formula |
---|---|---|
1RM_Rep | Brzycki [10] | L1RM_REP = L× (36/(37 − rep)) |
1RM_Mean | Jovanović and Flanagan [14] | L1RM_Mean = (MVT − interceptVMean)/slopeVMean |
1RM_Peak | Sayers, Schlaeppi, Hitz and Lorenzetti [15] | L1RM_Peak = (MVT − interceptVPeak)/slopeVPeak |
Exercise | N | TRUE | FALSE | NILL | %TRUE |
---|---|---|---|---|---|
Barbell bench press | 119 | 115 | 3 | 1 | 96.5% |
Barbell back squat | 121 | 98 | 10 | 13 | 76.5% |
Barbell deadlift | 124 | 115 | 4 | 5 | 92.2% |
Total | 363 | 327 | 17 | 19 | 88.4% |
Exercise | N | TRMean | RRMean | RMSE | p-Value | Pearson |
---|---|---|---|---|---|---|
Barbell bench press | 115 | 8.84 ± 2.01 | 9.44 ± 3.15 | 1.36 ± 2.16 | 0.01 | 0.61 |
Barbell back squat | 98 | 9.47 ± 1.20 | 9.31 ± 4.09 | 2.51 ± 3.08 | 0.68 | 0.24 |
Barbell deadlift | 115 | 9.41 ± 1.54 | 9.97 ± 3.80 | 2.57 ± 2.47 | 0.09 | 0.37 |
Total | 327 | 9.23 ± 1.59 | 9.58 ± 3.67 | 2.14 ± 2.56 | 0.06 | 0.40 |
Exercise | N | Attempts | Predicted | % Success |
---|---|---|---|---|
Barbell bench press | 30 | 30 | 2 | 6.7% |
Barbell back squat | 30 | 30 | 1 | 3.3% |
Barbell deadlift | 30 | 30 | 3 | 10% |
Total | 90 | 90 | 6 | 8.9% |
Exercise | Paired Algorithms | R2 | RMSE | p-Value | Pearson |
---|---|---|---|---|---|
Barbell bench press | 1RM_Mean, 1RM_Rep | 0.9799 | 4.63 | <0.01 | 0.99 |
1RM_Peak, 1RM_Rep | 0.9791 | 4.00 | 0.04 | 0.99 | |
1RM_Peak, 1RM_Mean | 0.9846 | 5.10 | <0.01 | 0.99 | |
Barbell back squat | 1RM_Mean, 1RM_Rep | 0.8129 | 13.86 | <0.01 | 0.90 |
1RM_Peak, 1RM_Rep | 0.7919 | 19.76 | <0.01 | 0.89 | |
1RM_Peak, 1RM_Mean | 0.9163 | 11.41 | <0.01 | 0.96 | |
Barbell deadlift | 1RM_Mean, 1RM_Rep | 0.6540 | 22.71 | <0.01 | 0.89 |
1RM_Peak, 1RM_Rep | 0.7078 | 21.82 | 0.01 | 0.84 | |
1RM_Peak, 1RM_Mean * | 0.8094 | 16.90 | 0.68 | 0.90 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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/).
Share and Cite
Oberhofer, K.; Erni, R.; Sayers, M.; Huber, D.; Lüthy, F.; Lorenzetti, S. Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting. Sports 2021, 9, 118. https://doi.org/10.3390/sports9090118
Oberhofer K, Erni R, Sayers M, Huber D, Lüthy F, Lorenzetti S. Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting. Sports. 2021; 9(9):118. https://doi.org/10.3390/sports9090118
Chicago/Turabian StyleOberhofer, Katja, Raphael Erni, Mark Sayers, Dominik Huber, Fabian Lüthy, and Silvio Lorenzetti. 2021. "Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting" Sports 9, no. 9: 118. https://doi.org/10.3390/sports9090118
APA StyleOberhofer, K., Erni, R., Sayers, M., Huber, D., Lüthy, F., & Lorenzetti, S. (2021). Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting. Sports, 9(9), 118. https://doi.org/10.3390/sports9090118