Activity Monitoring Through Wireless Sensor Networks Embedded Into Smart Sport Equipments: The Nordic Walking Training Utility
Author(s)
Language
English
Obiettivo Specifico
7TM.Sviluppo e Trasferimento Tecnologico
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Journal
Issue/vol(year)
/22 (2022)
ISSN
1530-437X
Publisher
IEEE
Pages (printed)
2744 - 2757
Date Issued
February 1, 2022
Abstract
This paper presents the study of NordicWalking providing objective evaluations based on real time acquisition of kinematic parameters during the sport practice. It is possible to carefully monitor the athletic gesture through the integration of conventional poles with inertial sensors, composed of a triaxial accelerometer, a triaxial gyroscope, a pressure sensor positioned on the handle, and a load cell, which constitute aWireless Sensor Networkwhose nodes are appropriately synchronized. The integration of such sensors, whichmust be unobstructive and not change the functionality of the poles, is dictated by the ultimate goal of providing a real time biofeedback in two possible scenarios. The first is intended for Nordic Walking’s instructors, who have the opportunity to verify the proper practice execution by their trainees through the availability of real time objective data, in addition to their personalexperience.The second is devoted to amateur playerswho can practicealone, after the training sessionwith the instructor, and can independently correct any imperfections in real time using a software tool running on their smartphone. Using the Dynamic TimeWarping algorithm, the proposed system identifies themost frequent errors in performing athletic gesture, allowing adjustment in real time of the sporting exercise, through the detection, quantification and correction of errors. The obtained results show that the developed system is able to provide an accurate analysis of the athletic gesture and the proposed algorithm allows a quantitative monitoring of the progress achieved by each subject over time.
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Technol., pp. 1–12, Jun. 2020.
using wearable sensors,” IEEE Commun. Surveys Tuts., vol. 15, no. 3,
pp. 1192–1209, 3rd Quart. 2013.
[2] E. De-La-Hoz-Franco, P. Ariza-Colpas, J. M. Quero, and M. Espinilla,
“Sensor-based datasets for human activity recognition—A systematic
review of literature,” IEEE Access, vol. 6, pp. 59192–59210, 2018.
[3] P. Downward, K. Hallmann, and S. Rasciute, “Exploring the interrelationship
between sport, health and social outcomes in the U.K.:
Implications for health policy,” Eur. J. Public Health, vol. 28, no. 1,
pp. 99–104, Feb. 2018.
[4] J. Zhao and G. Li, “Study on real-time wearable sport health
device based on body sensor networks,” Comput. Commun., vol. 154,
pp. 40–47, Mar. 2020.
[5] M. Magno, L. Benini, C. Spagnol, and E. Popovici, “Wearable low
power dry surface wireless sensor node for healthcare monitoring
application,” in Proc. IEEE 9th Int. Conf. Wireless Mobile Comput.,
Netw. Commun. (WiMob), Oct. 2013, pp. 189–195.
[6] M. Al-Amri, K. Nicholas, K. Button, V. Sparkes, L. Sheeran, and
J. L. Davies, “Inertial measurement units for clinical movement analysis:
Reliability and concurrent validity,” Sensors, vol. 18, no. 3, p. 719, 2018.
[7] J. Connolly, J. Condell, B. O’Flynn, J. T. Sanchez, and P. Gardiner,
“IMU sensor-based electronic goniometric glove for clinical finger
movement analysis,” IEEE Sensors J., vol. 18, no. 3, pp. 1273–1281,
Nov. 2017.
[8] P. Pierleoni et al., “A wearable fall detector for elderly people based
on AHRS and barometric sensor,” IEEE Sensors J., vol. 16, no. 17,
pp. 6733–6744, Sep. 2016.
[9] P. Pierleoni, A. Belli, L. Palma, M. Pellegrini, L. Pernini, and S. Valenti,
“A high reliability wearable device for elderly fall detection,” IEEE
Sensors J., vol. 15, no. 8, pp. 4544–4553, Aug. 2015.
[10] A. Agostinelli et al., “CaRiSMA 1.0: Cardiac risk self-monitoring
assessment,” Open Sports Sci. J., vol. 10, no. 1, pp. 179–190, Oct. 2017.
[11] C. Russo, F. Mocera, and A. Soma, “MEMS sensors for sport engineer
applications,” Proc. IOP Conf. Ser., Mater. Sci. Eng., vol. 1038, no. 1,
2021, Art. no. 012056.
[12] A. Ahmadi, D. D. Rowlands, and D. A. James, “Development of
inertial and novel marker-based techniques and analysis for upper arm
rotational velocity measurements in tennis,” Sports Eng., vol. 12, no. 4,
pp. 179–188, Aug. 2010.
[13] D. Connaghan, P. Kelly, N. E. O’Connor, M. Gaffney, M. Walsh, and
C. O’Mathuna, “Multi-sensor classification of tennis strokes,” in Proc.
IEEE SENSORS, Oct. 2011, pp. 1437–1440.
[14] J. Chardonnens, J. Favre, F. Cuendet, G. Gremion, and K. Aminian,
“A system to measure the kinematics during the entire ski jump sequence
using inertial sensors,” J. Biomech., vol. 46, no. 1, pp. 56–62, Jan. 2013.
[15] A. Ahmadi, F. Destelle, D. Monaghan, N. E. O’Connor, C. Richter, and
K. Moran, “A framework for comprehensive analysis of a swing in sports
using low-cost inertial sensors,” in Proc. IEEE SENSORS, Nov. 2014,
pp. 2211–2214.
[16] A. Umek, Y. Zhang, S. Tomažiˇc, and A. Kos, “Suitability of strain gage
sensors for integration into smart sport equipment: A golf club example,”
Sensors, vol. 17, no. 4, p. 916, Apr. 2017.
[17] Y. Nemoto, R. Sakurai, S. Ogawa, K. Maruo, and Y. Fujiwara, “Effects of
an unsupervised Nordic walking intervention on cognitive and physical
function among older women engaging in volunteer activity,” J. Exerc.
Sci. Fitness, vol. 19, no. 4, pp. 209–215, Oct. 2021.
[18] P. Kocur et al., “Does Nordic walking improves the postural control and
gait parameters of women between the age 65 and 74: A randomized
trial,” J. Phys. Therapy Sci., vol. 27, no. 12, pp. 3733–3737, 2015.
[19] J. Polecho´nski, W. Mynarski, and A. Nawrocka, “Applicability of
pedometry and accelerometry in the calculation of energy expenditure
during walking and Nordic walking among women in relation
to their exercise heart rate,” J. Phys. Therapy Sci., vol. 27, no. 11,
pp. 3525–3527, 2015.
[20] H. Figard-Fabre, N. Fabre, A. Leonardi, and F. Schena, “Physiological
and perceptual responses to Nordic walking in obese middle-aged
women in comparison with the normal walk,” Eur. J. Appl. Physiol.,
vol. 108, no. 6, pp. 1141–1151, Apr. 2010.
[21] M.-K. Breyer et al., “Nordic walking improves daily physical activities
in COPD: A randomised controlled trial,” Respiratory Res., vol. 11,
no. 1, p. 112, Dec. 2010.
[22] D. Homma, H. Jigami, and N. Sato, “Effects of Nordic walking
on pelvis motion and muscle activities around the hip joints of
adults with hip osteoarthritis,” J. Phys. Therapy Sci., vol. 28, no. 4,
pp. 1213–1218, 2016.
[23] M. Kunysz-Rozborska and A. Rejman, “Nordic walking as a form
of recreation,” Central Eur. J. Sport Sci. Med., vol. 26, no. 2,
pp. 77–82, 2019.
[24] F. Mocera, G. Aquilino, and A. Somà, “Nordic walking performance
analysis with an integrated monitoring system,” Sensors, vol. 18, no. 5,
p. 1505, May 2018.
[25] P. Pierleoni, A. Belli, L. Palma, M. Paoletti, S. Raggiunto, and F. Pinti,
“Postural stability evaluation using wearable wireless sensor,” in Proc.
IEEE 23rd Int. Symp. Consum. Technol. (ISCT), Jun. 2019, pp. 256–260.
[26] P. Pierleoni et al., “Biological age estimation using an eHealth system
based on wearable sensors,” J. Ambient Intell. Humanized Comput.,
vol. 12, no. 4, pp. 4449–4460, Apr. 2021.
[27] L. Cecchetto, Nordic walking e salute. Esperienze e strumenti di
educazione alla salute e riabilitazione-animazione. RSA-RSD, Centri
diurni, territorio, vol. 135. Santarcangelo di Romagna, Italy: Maggioli
Editore, 2014, pp. 69–76.
[28] A. Pezzatini, “Scopriamo la forza del bastoncino sul terreno,”
Camminare, Fusta Editore, Saluzzo, Italy, Tech. Rep. 39, 2013,
pp. 29–30.
[29] Y.-C. Wu, Q. Chaudhari, and E. Serpedin, “Clock synchronization of
wireless sensor networks,” IEEE Signal Process. Mag., vol. 28, no. 1,
pp. 124–138, Jan. 2011.
[30] S. O. Madgwick, A. J. Harrison, and R. Vaidyanathan, “Estimation of
IMU and marg orientation using a gradient descent algorithm,” in Proc.
IEEE Int. Conf. Rehabil. Robot. (ICORR), Jun. 2011, pp. 1–7.
[31] H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization
for spoken word recognition,” IEEE Trans. Acoust., Speech, Signal
Process., vol. ASSP-26, no. 1, pp. 43–49, Feb. 1978.
[32] R. Muscillo, M. Schmid, and S. Conforto, “The median point DTW
template to classify upper limb gestures at different speeds,” in Proc.
4th Eur. Conf. Int. Fed. Med. Biol. Eng. Berlin, Germany: Springer, 2009,
pp. 63–66.
[33] R. Muscillo, M. Schmid, S. Conforto, and T. D’Alessio, “Early recognition
of upper limb motor tasks through accelerometers: Real-time implementation
of a DTW-based algorithm,” Comput. Biol. Med., vol. 41,
no. 3, pp. 164–172, Mar. 2011.
[34] S. Wudarczyk, B. Lewandowski, J. Szrek, and J. Bałchanowski, “A simulation
stand for human limb movements during Nordic walking,” in
Proc. Int. Conf. Theory Mach. Mech. Cham, Switzerland: Springer, 2020,
pp. 173–182.
[35] C. Russo, F. Mocera, and A. Somà, “Nordic walking multibody analysis
and experimental identification,” Proc. Inst. Mech. Eng., P, J. Sports Eng.
Technol., pp. 1–12, Jun. 2020.
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