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Authors: Pierleoni, Paola* 
Raggiunto, Sara* 
Marzorati, Simone* 
Palma, Lorenzo* 
Cucchiarelli, Alessandro* 
Belli, Alberto* 
Title: Activity Monitoring Through Wireless Sensor Networks Embedded Into Smart Sport Equipments: The Nordic Walking Training Utility
Journal: IEEE Sensors Journal 
Series/Report no.: /22 (2022)
Publisher: IEEE
Issue Date: 1-Feb-2022
DOI: 10.1109/JSEN.2021.3136760
Keywords: Activity monitoring
data analytics
Internet Of Things
smart sport equipments
wireless sensor network
Subject Classification05.04. Instrumentation and techniques of general interest 
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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