Analytics for Personal Fitness Devices
Analytics in health care is yet an undiscovered territory, but due to IoT devices it is estimated to grow to $53 billion in the next three years. Here we explain the current status of industry, its future potential and key drivers.
The personal fitness device industry is changing with the advent of the Internet of Things (IoT). Previously, the personal fitness devices were just devices, isolated, doing a specific job such as recording your blood pressure. You could either view the information or share it with your doctor. Else, there was no way for the information to spread. But the IoT has changed things.
Now, fitness devices come fitted with a sensor and have an IP address which enables the devices to send data over the Internet. The data stream could enable doctors and healthcare providers provide better healthcare and device makers objectively measure the performance of their devices. The data collected is being converted into analytics to gain better insights. The demands on the devices continue to evolve and make things more complex for the device makers every day. For example, it is no longer enough to just collect data. A device should be able to notify a patient that a hospital visit is required if the data crosses a certain threshold.
Personal fitness devices defined
The definition for personal fitness device has gone beyond those devices that were just part of your daily exercise programs — heart rate, blood pressure and calorie monitors, for example. Of course, these devices continue to be fitness devices. But the purview of fitness devices has extended and could do a lot of things such as the following:
- Continuous glucose
- Blood pressure
- Infant care
- Oxygen level
- Eye tracking
- Brain activity
- Radiation exposure
- Pain relief
- Skin conductance
It is worthwhile to mention the names of some innovative personal fitness devices. Of course, the famous Apple Watch has been the trend-setter.
- Lumbo Lift: Keeps a watch over your posture. Sends an alert if you slouch.
- Jabra’s Heartrate Tracking Earbuds: This measures heart rate but you need to wear it on your ears and not on your hands.
- Mimo Baby Monitor: It measures skin temperature, respiration, sleeping, body position, and activity of infants.It is washable and you can monitor your baby from anywhere on the world.
- Withings Aura Sleep Tracking Device: This device tracks light sleep, REM, total sleeping duration and wakeups and also gives suggestions on improving sleep quality. It can also do daytime activity monitoring.
It is obvious that the fitness device industry is evolving and it is just the start. It is estimated that the market for medical devices may reach $53 billion in the next three years. Smart watches are going to dominate the sale of such devices with the Apple Watch alone accounting for 40% of the sale. Even if the Apple appeal factor is discounted, the market for medical devices is quite lucrative.
Surveys over a period of time have revealed the following insights:
Hospitals are still in the beginning stages of appreciating the power of data from devices.
Fig. 1: manual process of data collection
Hospitals are yet to realize the importance of personal fitness devices, as evident from the below image.
Fig. 2: Usage of medical devices
On a different note, given the areas these devices cover now and even the areas that might be covered in the future, the amount of data all devices will generate will be mind-boggling. Obviously, the data is a veritable goldmine but to leverage its potential, you need a good analytics platform.
Analytics for personal fitness devices and its advantages
To get the best out of the data generated by the personal fitness devices, you need an integrated analytics platform. Isolated tools and platforms are not going to be sufficient at handling the data onslaught. You cannot rely on the traditional Relational Database Management System (RDBMS). The integrated analytics platform combines disparate technologies and tools such as Database Management System (RDBMS), execution engine, data mining capabilities and the ability to procure and ready information that does not exist in the database. The main context of an integrated analytics platform is that the data from the hugely varied fitness devices are going to be absolutely varied and unstructured and normal tools do not have the capability to interpret the data. The main challenge is to deliver meaningful data and quickly. So, a unified solution is the need of the hour. As already stated in this article, an integrated analytics platform can unite a database management system, data collection and storage system and processing capabilities. Here are a few strong reasons an integrated analytics platform can deliver meaningful data out of the IoT data.
Designed to accommodate new data types
The platforms can bind together several commodity processors that have large storage spaces. This helps to scale operations. Considering the varied fitness devices and their application areas, it is possible that the data they generate can vary wildly in terms of format, file types and size. You need a platform that can accommodate and process such data. An integrated platform can retrieve information with columns and can encode data to guarantee superior compression. Some platforms can use the feature of “smart storage” that enables analytical processors to delegate heavy analytical lifting and focus on data analysis and parsing which finally improves performance and speed and delivers high quality analytics.
Advanced and quality analytics
Integrated analytics platforms can do advanced analysis on data from fitness devices and deliver advanced analytics. Just to do a comparison, regular analytics tools might struggle to perform a simple comparison of the combined health parameters of all patients in a clinic in the last 10 days. Integrated analytics, on the other hand, can deliver that analytics easily and provide much more. Based on the data received from different sources, it can build up predictive data models based on which health prescriptions can be delivered in a customized manner. Hospitals can preserve the health data models and modify it with more data patterns in the future.
Cost saving and convenient
Connected healthcare, combined with integrated analytics, can save a lot of costs and improve convenience and comfort. The healthcare providers and doctors can provide better and more importantly, accurate data-based healthcare to patients. What is extremely important is that based on the comprehensive, high-quality analytics, patients can expect to receive a comprehensive healthcare. For example, devices can record the psychological state of patients which can impact the physiological state as well. The doctors can review the information in real-time basis and provide accurate healthcare to the patients. Other benefits include lesser visits to the clinic, accuracy in analysis, preservation of large amount of historical data and reduction in dependency on a single doctor because the information is available digitally. Overall, the integrated analytics framework can deliver a win-win situation for both the healthcare providers, device makers and the patients.
The industry for personal fitness devices has changed both in definition and in its scope for providing healthcare, thanks to IoT. However, it is the beginning stage and there are issues inhibiting the growth of such devices. Many people still consider these devices costly and refuse to see the benefits they offer. Second, as shown by the images earlier, hospitals are yet to realize the potential in the fitness devices. Third, these devices are yet to establish their credentials when it comes to ensuring the security and confidentiality of data. So, there is still time before these devices dominate the market.
Bio: Kaushik Pal (www.techalpine.com) has 16 years of experience as a technical architect and software consultant in enterprise application and product development. He has interest in new technology and innovation area along with technical writing. His main focuses are on web architecture, web technologies, java/j2ee, Open source, big data and semantic technologies.
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