Quantifiable fitness tracking using wearable devices

Author(s):  
Anurag Bajpai ◽  
Vivek Jilla ◽  
Vijay N. Tiwari ◽  
Shankar M. Venkatesan ◽  
Rangavittal Narayanan
Author(s):  
Junqing Xie ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Yuxi Jia ◽  
Li Gao ◽  
...  

BACKGROUND Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. OBJECTIVE The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. METHODS A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). RESULTS Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). CONCLUSIONS At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.


Author(s):  
Joel R. Drake ◽  
Ryan Cain ◽  
Victor R. Lee

Wearable technologies represent a rapidly expanding category of consumer information and communications technologies. From smartwatches to activity tracking devices, wearables are finding their way into many aspects of our lives, changing the way we think about ourselves and the world around us. The rapid adoption of these tools in everyday life hints at the possibilities these devices may hold in school and other educational settings. Drawing on examples taken from a five-year study using wearable fitness tracking devices in elementary and middle school classrooms, this paper presents two examples of how wearable devices can be appropriated for use in school settings. These examples focus on instances where students turned activity trackers into objects of inquiry using data from familiar activities.


2018 ◽  
Vol 15 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Sowmini Sengupta ◽  
Jisun Kim ◽  
Seong Dae Kim

This paper describes the application of a combination of TRIZ and Bass modeling to forecast the technology growth projections for one of the wearable devices, fitness trackers. For the TRIZ modeling, the fitness tracking system was divided into three subsystems and each was analyzed as per the technology trends from current literature. The subsystems’ combined assessment was then visualized via a radar plot. The analysis showed the technology to be in an emergent state with primary growth in the hardware and software subsystem areas. The Bass model showed the market peaking at eight and saturating in 15 years.


Author(s):  
Saeed Ahmed Khan ◽  
Shamsuddin Lakho ◽  
Ahmed Ali ◽  
Abdul Qadir Rahimoon ◽  
Izhar Hussain Memon ◽  
...  

Most of the emerging electronic devices are wearable in nature. However, the frequent changing or charging the battery of all wearable devices is the big challenge. Interestingly, with those wearable devices that are directly associated with the human body, the body can be used in transferring or generating energy in a number of techniques. One technique is triboelectric nanogenerators (TENG). This chapter covers different applications where the human body is used as a triboelectric layer and as a sensor. Wearable TENG has been discussed in detail based on four basic modes that could be used to monitor the human health. In all the discussions, the main focus is to power the wearable healthcare internet of things (IoT) sensor through human body motion based on self-powered TENG. The IoT sensors-based wearable devices related to human body can be used to develop smart body temperature sensors, pressure sensors, smart textiles, and fitness tracking sensors.


Author(s):  
Katherine Chen ◽  
Mary Zdorova ◽  
Dan Nathan-Roberts

Self-quantifying fitness tracking services and wearable devices have become more ubiquitous but still suffer from high abandonment rates. Wearable devices can monitor the physiological changes of the body and alert patients and physicians of immediate needed actions and facilitate in the research process of disease prevention and treatment, especially in areas that have less access to healthcare. This proceeding discusses how to bridge the gap between novice and expert users by implementing gamification techniques and social communities within fitness tracking services to increase the enjoyment, perceived ease of use, perceived usefulness, and long-term engagement of fitness tracking services. Additionally, retention of wearable technology can be improved by applying human factors principles to make products more user-centered and friendly.


Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 91
Author(s):  
Shun Ishii ◽  
Anna Yokokubo ◽  
Mika Luimula ◽  
Guillaume Lopez

Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.


2018 ◽  
pp. 810-832
Author(s):  
Joel R. Drake ◽  
Ryan Cain ◽  
Victor R. Lee

Wearable technologies represent a rapidly expanding category of consumer information and communications technologies. From smartwatches to activity tracking devices, wearables are finding their way into many aspects of our lives, changing the way we think about ourselves and the world around us. The rapid adoption of these tools in everyday life hints at the possibilities these devices may hold in school and other educational settings. Drawing on examples taken from a five-year study using wearable fitness tracking devices in elementary and middle school classrooms, this paper presents two examples of how wearable devices can be appropriated for use in school settings. These examples focus on instances where students turned activity trackers into objects of inquiry using data from familiar activities.


2018 ◽  
Vol 6 (4) ◽  
pp. e94 ◽  
Author(s):  
Junqing Xie ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Yuxi Jia ◽  
Li Gao ◽  
...  

Photoplethysmography (PPG) technique is used in most of the fitness tracking devices available now-a-days, due to its low cost and simplicity. These PPG signals are obtained from the variations in the blood flow with the help of pulse rate sensor. In this paper, PPG signal is acquired from two PPG sensors worn in the finger and the wrist to get the heart rate and its various parameters. The signal noise is removed with the help of MATLAB with various filters to get the smooth signal. The peak detection is done for heart rate (HR) and peak interval calculation, the spectral estimation is done, the first and second derivatives and the heart rate variability are obtained. The low cost Arduino nano and the Bluetooth module is used for the development and the transmission of HR value from the wearable device through an application developed for it, as well as the values are transmitted remotely with the help of the Global System for Communication (GSM) built in the mobile phone, which can display the value and can also transmit it to any particular person or remote physician who can monitor the person’s HR remotely. The complete system developed provides a low cost solution for heart rate detection and monitoring of a person from a distance.


Sign in / Sign up

Export Citation Format

Share Document