Green Communication for Tracking Heart Rate with Smartbands

Abstract

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user.

Gait Assessment of Younger and Older Adults with Portable Motion-Sensing Methods: A User Study

“…Gonzálezlandero et al [2] used a smart bracelet Sony 2 for measuring heart rate and Google Fit Application Programming Interface for storing data and Android for managing data, while this study have explored the use of the smart bracelet to measure gait characteristics based on its embedded motion sensor. ese studies suggested that smart bracelets could be applied to measure several body features as health management indicators.…”

“…us, gait assessment was performed over 10 m. Two tapes were fixed on the start line and the finish line as markers. e instructions were given to each participant in a standard form, as follows: (1) slow: walk very slowly, as if you were walking in a park; (2) normal: walk at your normal speed to the terminal line; (3) fast: walk as fast as you can to the finish line, however, do not run or take risks. e sequence of the gait speed was randomized to avoid the effect of the order.…”

“…Wearable devices for older adults with the function of health management have become popular in recent years. Smart bracelets, such as the Jawbone UP, the Fitbit Flex, and the Garmin Vivofit, allow users to track their activities, nutrition, sleeping patterns, [1] or heart rate [2]. Moreover, many wearable devices with built-in accelerometers, such as smart bracelets [3], iPods [4], and smartphones [5], can obtain more elaborate gait characteristics to detect subtle gait changes.…”

“…More specifically, we explored the following two research objectives: (1) to test feasibility of measuring gait patterns among younger and older Chinese adults using portable motion-sensing methods; (2) to dig user requirements to improve the user experience of the motion-sensing system. e system described in this study has the potential to be expanded to a telemedicine service.…”

Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

“…Each person wore a smartwatch which registered, continuously, the exercise done (number of steps), heart rate, and minutes of sleep. Although these devices are not specifically medical devices, the accuracy is good enough for the input to be considered as valid, and the energy consumption requirements are becoming lower and lower [46]. Further characteristics can be found on the manufacturer's website.…”