Forecasting New Features and Market Adoption of Wearable Devices Using TRIZ and Growth Curves: Case of Fitness Tracking Products

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):  
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.


2021 ◽  
pp. 1-13
Author(s):  
Dan Xie ◽  
Ming Zhang ◽  
Priyan Malarvizhi Kumar ◽  
Bala Anand Muthu

The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.


Author(s):  
Michael Schwartz ◽  
Paul Oppold ◽  
P. A. Hancock

Prior research has reported that novelty affects the usage cycle of wearable devices. This chapter investigates the effects of sensation seeking, intensity, novelty, gender, and prior experience on the workload experienced during one aspect of using wearable fitness trackers, the device installation process. Contrary to the authors' hypotheses, prior experience, sensation seeking, intensity, and novelty did not significantly affect workload. The findings suggest that males tend to experience less workload during the setup of wearable fitness trackers; however, only for the Basis B1 and only for some aspects of workload. The claims made by prior research may be limited to specific aspects of the wearable fitness tracker use cycle, and more investigation is needed before broader claims can be made.


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.


Author(s):  
Jiska Classen ◽  
Daniel Wegemer ◽  
Paul Patras ◽  
Tom Spink ◽  
Matthias Hollick

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5350 ◽  
Author(s):  
Yuxi Jia ◽  
Wei Wang ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Li Gao ◽  
...  

Background There are many problems with fitness trackers, such as device usability, which limit their large-scale application, and relevant studies are limited in terms of their sample size and evaluation methods. The purpose of the study was to evaluate the perceived usability of various mainstream fitness trackers on the market, and to learn about user feedback on feature preferences for each device. Methods Trial use of seven mainstream fitness trackers (two smart watches and five smart wristbands) followed by a survey study were applied. The questionnaire was specifically developed for this study, which included two parts (user preferences and device usability in five dimensions). We recruited users to test the devices for at least 30 days and asked experienced users to provide feedback in order to evaluate each device, including the rating and user preference of each device. Results We received 388 valid questionnaires, in which users rated their responses on a five-point Likert scale. (1) User preference: the average user satisfaction was 3.50–3.86 (points), and the rating for willingness to buy averaged between 3.36 and 3.59. More users were willing to wear (58.3–81.3%) and purchase (56.8–83.0%) the devices than were not. The top three general feature preferences were daily activity tracking, heart health monitoring, and professional fitness tracking. The top three health-related feature preferences were heart rate monitoring, daily pedometer, and professional fitness tracking. (2) Usability evaluation: product design was rated from 3.57 to 4.00; durability, 3.63–4.26; ease of use, 3.70–3.90; added features, 3.30–3.83; and user-rated accuracy, 3.44–3.78. A significant difference was observed in the rating of product design and durability among the different devices (p < 0.05) score. Conclusions Users generally had positive subjective intent regarding fitness trackers but were less satisfied with their cost effectiveness. The users preferred health related features such as heart health monitoring, and professional fitness tracking. The rating of most of the current mainstream fitness trackers was fair with some significant differences among the devices. Thus, further improvement is needed.


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.


Author(s):  
Anurag Bajpai ◽  
Vivek Jilla ◽  
Vijay N. Tiwari ◽  
Shankar M. Venkatesan ◽  
Rangavittal Narayanan

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