Wearable Sports Research Articles

Customer segmentation based on activity monitoring applications for the recommendation system

Nowadays, wearable sports performance tracking devices are gaining tremendous popularity due to intense advertising and innovative health benefits they are supposed to provide. Fashion and the opportunity to show off your results on social media are also important. On the other hand, the data recorded and shared by these devices may serve as an exciting source of customer insight for sports apparel and equipment marketers. In this study, the author proposes incorporating the data on customers’ physical activity into traditional recommendation systems. The focus was on recommendations for people who actively jog or run. The data for the recommendation system in the discussed case should characterize the specificity of the activity and be semantically related to products recommended to a specific group of people. Therefore, the analysis relies on the user activity profile and parameters of the products. The study presented in this paper introduces an analysis of an experimental dataset acquired from 210 active joggers and runners who use different sport-tracking applications. The study participants are potential customers for sports apparel and specialized types of running shoes. The groups of users were identified according to the most meaningful parameters of their workouts. Clustering and different classification models were tested with Orange software, and basic rules of division were defined. The study shows that quite an accurate user segmentation is possible and indicates its further improvement. Combining different knowledge models describing customer behavior and characteristics from various perspectives may lead to acquiring valuable and unique marketing knowledge.

Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

According to the development needs of wireless sensor networks, this paper uses the combination of embedded system and wireless sensor network technology to design a network node platform. This platform is equipped with a sports training sensor module to measure the physiological indicators of the ward in real time. The network node sends the collected physiological parameters to a remote monitoring center in real time. First, according to the generation mechanism of the physiological index signal and the characteristics of the physiological index signal, the wireless sensor network analysis and processing method are used to denoise the physiological index signal, and the wireless sensor network package is used to extract the characteristics of the physiological index, indicating different types of respiration. The energy characteristics of the sound physiological index signals are different, which verifies the feasibility of the independent component analysis method for separating the physiological index and the physiological index signal of the heart sound. Secondly, the hardware system of physiological index signal acquisition is designed, and the selection principle of the hardware unit is introduced. At the same time, the system structure of the monitor is designed, and then, the wireless sensor network sensor node is researched, the hardware of the wearable monitor system is designed, and the hardware architecture and working mode based on the single‐chip MSP430F149 are given. Finally, the wireless hardware platform includes the following main modules: sensor part, preprocessing circuit module, microprocessing module based on MSP430 low power consumption, wireless transceiver module based on RF chip CC2420, and power supply unit used to provide energy.

Design and data analysis of wearable sports posture measurement system based on Internet of Things

Aiming at sports and daily training of athletes in rehabilitation therapy, a wearable sports posture measurement system is proposed in combination with the Internet of Things technology. By choosing a professional coach to wear the system, the experiment was verified with common sports postures, and the actual measured sports information was analysed and processed. The experimental results show that the system can perform stable online monitoring of the exercise process, and the data analysis results have a certain reference value for the evaluation of the patient's physical condition during rehabilitation and the daily training evaluation of athletes.

Data processing platform design and algorithm research of wearable sports physiological parameters detection based on medical internet of things

• Physiological parameter detection in the data processing platform through the built-in algorithm. • The physiological parameter array is obtained and transmitted to ZigBee network by platform. • It can realize the remote and real-time information interaction between doctors and patients. In this paper, the data processing platform for the detection of wearable Sports Physiological Parameters Based on the medical Internet of things can be connected with the network to build a medical Internet of things platform for remote monitoring of patients' lives, which can process physiological data in real time. Finally, the important physiological parameter array of human body is obtained and transmitted to ZigBee network in real time. If the patient needs further diagnosis, the collected data can also be sent to the home gateway through ZigBee and then sent to the telemedicine center through internwt for diagnosis by the doctor and the diagnosis results will be sent back to the patient. The data processing platform can realize the remote and real-time information interaction between doctors and patients.

Photosensitive nanofibers for data recording and erasing

Photosensitive nanofibers have been utilized in various applications such as everyday apparels, protective clothing, medical and sports wearables. Polyurethane (PU) electrospun nanofiber mats (ENMs) are considered as one of the strong, stretchable, breathable, hydrophobic and durable textile substrates having good coloration properties. The photosensitive spiropyran dye[1′-3′-dihydro-8-methoxy-1′,3′,3′-trimethyl-6-nitrospiro2H-1-benzopyran-2,2′-(2H)-Indole] (Ind) was successfully incorporated into PU and polystyrene (PS) nanofibers via electrospinning. The resultant ENMs were measured upon alternate irradiation with ultraviolet at wavelength of 365 nm for coloration and with visible light at (40 °C) for decoloration on a designed photomask of quick-response code at the minimum area (1 × 1 cm2) with multiple photo-switching. SEM images revealed smooth morphology of the Ind-PS ENM and Ind-PU ENM with a slight increase in fiber diameter. The results revealed that the Ind-PS ENM and Ind-PU ENM can potentially be considered for data recording/erasing or where such type of ENMs can be utilized.Photocoloration/Decoloration of PU-Ind and PS-Ind ENMs

Individuals' intention to use sports wearables: the moderating role of technophobia

PurposeThis study aims to examine the wearable devices market as an essential representative of the digital age using a framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the context of sports wearables.Design/methodology/approach411 people, are both users and non-users of this technology were surveyed online, and the obtained data analyzed using structural equation modeling.FindingsThe results support the effects of performance expectancy, effort expectancy, facilitating conditions, and social influence on attitude toward sports wearables and attitude of usage intention. Further, technophobia moderates the relationship between performance expectancy and attitude. However, a moderating effect of technophobia on the relationship between effort expectancy and attitude was not observed.Originality/valueDue to innovative technologies in the digital age we live in, the devices we use in everyday life have gained intelligence. As more developments take place, and related products enter the market, understanding how people react to these products becomes an important issue. While investigating this issue in the context of sports wearables in this study, an important psychological construct, technophobia, was included in the research model in order to explore the usage intention of individuals through the effects of psychological constructs, such as paranoia, fear, anxiety, cybernetic revolt and cellphone avoidance, and the strong combination of important constructs of phobia to go against technology.

Study on real-time wearable sport health device based on body sensor networks

As a branch of the wireless sensor network and an important part of the Internet of Things (IoT), Body Sensor Network (BSN) integrates biomedical engineering technology and wireless sensor network. It has high precision, small monitoring range, miniaturization and low power. The congruent feature can monitor the physiological information and motion information of the human body in real time, and provide the tester with many important quantitative information, to realize early monitoring, early prevention and early intervention of some common chronic diseases. This technology can be widely applied in biomedical, sports training, daily activity monitoring and other fields. In addition, the human sensor network is also an implementation of home health care, providing a new way of thinking for telemedicine and home health care. In order to realize the human motion monitoring system based on human body sensor network. This research aims to design a wearable sport health monitoring system with low cost, low power consumption, high modularity, high reliability, and high precision. The human body motion monitoring system of the axis wireless sensor platform enables real-time monitoring of sports health.

Self-Powered Wearable Sweat-Lactate Analyzer for Scheduling Training of Boat Race

Self-powered wearable sweat-lactate analyzer has been developed for training analysis of rowing. Tetrapodshaped ZnO nanowires are attached onto ordinary textiles to form a device. Based on the coupling of enzymatic reaction (lactate oxidase and lactic acid) and piezoelectric effect, the device can be attached to the tester to monitor the driving frequency, rowing distance and sweat lactic acid concentration of the tester in real-time. The relationship between the rowing frequency and physiological state is obtained by monitoring the rowing process of the tester, which is helpful to formulate a training plan belonging to the rowing tester. This study provides a new direction for the scientific monitoring of kinematics and accelerates the development of wearable sports equipment.

Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices

Internet of Things (IoT) devices have expanded the horizon of digital forensic investigations by providing a rich set of new evidence sources. IoT devices includes health implants, sports wearables, smart burglary alarms, smart thermostats, smart electrical appliances, and many more. Digital evidence from these IoT devices is often extracted from third party sources, e.g., paired smartphone applications or the devices' back-end cloud services. However vital digital evidence can still reside solely on the IoT device itself. The specifics of the IoT device's hardware is a black-box in many cases due to the lack of proven, established techniques to inspect IoT devices. This paper presents a novel methodology to inspect the internal software activities of IoT devices through their electromagnetic radiation emissions during live device investigation. When a running IoT device is identified at a crime scene, forensically important software activities can be revealed through an electromagnetic side-channel analysis (EM-SCA) attack. By using two representative IoT hardware platforms, this work demonstrates that cryptographic algorithms running on high-end IoT devices can be detected with over 82% accuracy, while minor software code differences in low-end IoT devices could be detected over 90% accuracy using a neural network-based classifier. Furthermore, it was experimentally demonstrated that malicious modification of the stock firmware of an IoT device can be detected through machine learning-assisted EM-SCA techniques. These techniques provide a new investigative vector for digital forensic investigators to inspect IoT devices.

Consumer acceptance of sports wearable technology: the role of technology readiness

PurposeThe purpose of this paper is to investigate consumers’ acceptance and use of sports and fitness wearable devices based on technology readiness (TR). In addition, the technology readiness and acceptance model (TRAM) will be used to investigate consumers’ intention to use sports wearable devices (for simplicity, sports wearable devices will be simplified to the term “sports wearables”).Design/methodology/approachConvenience sampling was conducted from Korean consumers (n=247). Data were analyzed by partial least squares–structural equation modeling using SmartPLS 3.0.FindingsThe results found that positive TR has a positive influence on perceived ease of use (PEOU) and perceived usefulness (PU), and negative TR had a negative influence on PEOU and PU. PEOU had a positive influence on perceived usefulness (PU). Both PEOU and PU led to intention to use sports wearable devices. Also, the multi-group analysis found a positive correlation between TR and PEOU for especially male users.Originality/valueThe findings of this study provide a better understanding of consumers’ behavioral intent to use sports wearables. Particularly, it also provides evidence that the TRAM is an appropriate framework for predicting users’ intention to use sports wearables. This study also stresses the important role of TR in consumers’ psychological processes leading up to the actual use of novel sports wearables.

Wearable Sport Activity Classification Based on Deep Convolutional Neural Network

This paper develops a wearable sport activity classification system and its associated deep learning-based sport activity classification algorithm for accurately recognizing sport activities. The proposed wearable system used two wearable inertial sensing modules worn on athletes’ wrist and ankle to collect sport motion signals and utilized a deep convolutional neural network (CNN) to extract the inherent features from the spectrograms of the short-term Fourier transform (STFT) of the sport motion signals. The wearable inertial sensing module is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. All ten participants wore the two wearable inertial sensing modules on their wrist and ankle to collect motion signals generated by sport activities. Subsequently, we developed a deep learning-based sport activity classification algorithm composed of sport motion signal collection, signal preprocessing, sport motion segmentation, signal normalization, spectrogram generation, image mergence/resizing, and CNN-based classification to recognize ten types of sport activities. The CNN classifier consisting of two convolutional layers, two pooling layers, a fully-connected layer, and a softmax layer can be used to divide the sport activities into table tennis, tennis, badminton, golf, batting baseball, shooting basketball, volleyball, dribbling basketball, running, and bicycling, respectively. Finally, the experimental results show that the proposed wearable sport activity classification system and its deep learning-based sport activity classification algorithm can recognize 10 sport activities with the classification rate of 99.30%.

직물형 피트니스 밴드 디자인 및 개발

This study aims to contribute to the development of sports wearables. It was conducted by a convergence team of professionals in the fashion industry, kinesiology and sports studies, and computer science and engineering. The purpose of the current study was to design and develop a fabric-type fitness band for a sensor to measure acceleration during jump rope exercises. Computer science and engineering professionals developed the Arduino board and sensor, kinesiology and sports studies provided the necessary exercise protocol, and the fashion industry professionals developed the band. First, a fitness band preference survey was completed by men and women between the ages of 20 and 50. Typical uses of the band included tracking exercise amount as measured by the number of steps taken and calories burned. Strap watch closure, a single color and achromatic color, and soft and smooth touch materials were preferred as band design. Second, two fabric-type fitness bands were designed and developed. Design 1 had a 3-dimensional pocket for the sensor, bright blue color, and stretch binding around the edges and for a loop. Design 2 had a flat pocket for the sensor, achromatic color, mesh binding around the edges and two metal loops. Both designs had Velcro as a closure. Third, wear testing of both bands with the sensor were conducted of 15 women in their 20s. They wore the bands during jump rope exercises. Both bands generally satisfied the participants. The Design 2 band was slightly more satisfying than the Design 1 band.

Respiratory Frequency during Exercise: The Neglected Physiological Measure.

The use of wearable sensor technology for athlete training monitoring is growing exponentially, but some important measures and related wearable devices have received little attention so far. Respiratory frequency (fR), for example, is emerging as a valuable measurement for training monitoring. Despite the availability of unobtrusive wearable devices measuring fR with relatively good accuracy, fR is not commonly monitored during training. Yet fR is currently measured as a vital sign by multiparameter wearable devices in the military field, clinical settings, and occupational activities. When these devices have been used during exercise, fR was used for limited applications like the estimation of the ventilatory threshold. However, more information can be gained from fR. Unlike heart rate, O2, and blood lactate, fR is strongly associated with perceived exertion during a variety of exercise paradigms, and under several experimental interventions affecting performance like muscle fatigue, glycogen depletion, heat exposure and hypoxia. This suggests that fR is a strong marker of physical effort. Furthermore, unlike other physiological variables, fR responds rapidly to variations in workload during high-intensity interval training (HIIT), with potential important implications for many sporting activities. This Perspective article aims to (i) present scientific evidence supporting the relevance of fR for training monitoring; (ii) critically revise possible methodologies to measure fR and the accuracy of currently available respiratory wearables; (iii) provide preliminary indication on how to analyze fR data. This viewpoint is expected to advance the field of training monitoring and stimulate directions for future development of sports wearables.

System Design for Wearable Blood Oxygen Saturation and Pulse Measurement Device

With the rapid development of information technology, the demands of sport monitoring research are rapidly increasing. Sport Internet of Things (IoTs) based on the sensor technique and modern communication technology are becoming more and more important to current sport training and daily activities. Sensor-based sport monitoring approaches lead people to take advantage of the wearable sport devices to monitor their own physiological parameters, in which we will focus on the blood oxygen saturation and the pulse measurement. In order to measure these two parameters, we will adopt near-infrared portable tissue oximeter with detection module using STM32 microprocessor. Near infrared spectroscopy (NIRS) is becoming a widely used research instrument to measure tissue oxygen (O2) status non-invasively. Continuous-wave spectrometers are the most commonly used devices, which provide semi-quantitative changes in oxygenated and deoxygenated hemoglobin in small blood vessels (arterioles, capillaries and venules). Through the measurement using the near-infrared technology, we could obtain the blood oxygen saturation and the pulse parameters. After obtaining the above two parameters, the portable device will send the data to the remote coaches or doctors via GPRS/WiFi/Zigbee networks. The data of the blood oxygen saturation and the pulse that is detected by the detection module are processed by the expert decision-making system in order to provide the coaches and doctors with the athlete condition. In addition, the data can be sent to the smart phones of the players, coaches or doctors through GPRS/WiFi networks, in which the blood oxygen saturation and the pulse data of the athletes can be displayed according to the requirement of the remote users. The test results show that the accuracy of measurement is high and the system is stable.

18.3: Fully Integrated CMOS Microdisplays for Wearable Sports and HMD Applications

AbstractLarge market growth in head‐mounted and wearable displays is predicted in the near future covering applications areas such as sport, security and entertainment. These application areas are often cost sensitive and form factor critical. In this paper two novel CMOS microdisplays are presented for application in wearable sports and head‐mounted display applications. The microdisplays are implemented in a standard 0.35 μm CMOS process requiring no post‐processing. The presented microdisplays offer a cost effective alternative to conventional LC and OLED‐based displays, while maintaining a small form factor.