Wearables Predictions: Who to Watch for Prediction #4

This is the fourth post in a Wearable Industry Watch Series for each of the 10 Wearables Predictions.  Follow this blog or Twitter handle @WorkTechWork to be notified of each part of the series. To view all predictions and links to the other parts of the series, visit the Wearable Industry Watch Series.

Prediction #4:  Wearables will become more intelligent because of developments in sensor technology and the ability to translate data from these sensors into insight via analytics.

Developments in Sensor Technology:

One of the challenges of wearable electronics is the rigidity of the components and the resulting space requirements to manage ridged components.  Consumers want small, comfortable devices but the inherent rigidity of metal and silicon require that components be kept from bending or flexing or else they can becoming brittle and break, a particularly challenging issue wearable textiles.

Dr. Wenlong Cheng and a team of researchers at Monash University in Melbourne, Australia have come up with a sensor that can bend or be twisted without cracking (read more here).  I had the opportunity to talk with Dr. Cheng about this technology last week and the conversation has me excited for potential applications in the wearable industry, as well as the wider Internet of Things because of this material’s fast response times, high sensitivity and stability under lots of different situations.  This technology could disrupt how all wearable devices are designed in the Biosensing Wearables Landscape described by Rock Health. (image source Rock Health)

RockReportWearables4 from Rock Health

Ability to Translate Sensor Data to Insight via Analytics:

Knowing how many steps taken in a day is one thing, being able to detect anomalies in behavior that resulted in more or less steps, or understanding how the number of steps taken in a day affects behavior, particularly spending behavior, is another and data scientist are working hard to translate sensor data into insight.

It is no easy task.  The Cityzen Sciences team produces D-Shirt, a smart shirt that generates 200,000 data points in one hour.   Big data suddenly became humongous data with such finite data for the quantified self.  Fortunately, the team also offers the Cityzen Data platform which enables data collection and storage so that value, created through analytics, can be created from sensor data.

Cityzen is not alone in analyzing wearable data.  Empath Analytics acquires data from multiple wearable devices and is positioned as a Backend-as-a-Service API to help developers create apps leveraging data from wearable devices.  Empath Analytics can collect, parse, and clean data as well as apply machine learning techniques enabling developers the luxury to focus on generating value for users and improving user experience.

Moreover, big players in the space including Google, Facebook, Microsoft, and others are acquiring sophisticated analytics producing companies with skilled data scientists almost as quick as they can incorporate and build websites.  All that glitters is not gold, though, because it is easy for a bus dev team member to say, “We can turn data into actionable insight” and difficult for a team to actually deliver on that promise.

On a lighter note, what is good for man may also be good for man’s best friend.  Pet wearable players like Whistle  and  Voyce are enabling the quantified pet and using data analytics to enrich the lives of both pets and pet owners.

Lastly, I’ll mention a point on the immense quantities of raw data (recall 200,000 data points and hour from one smart shirt!).  With large data sets, it becomes challenging to identify features in raw data that are meaningful.  Deep learning  may find attributes in quantified self data that humans simply cannot detect.

Are you aware of developments in sensor technology or are you working to translate sensor data into insight?  If so, I want to hear about it so express your thoughts in the comments below or reach out on the contact page.

Next Prediction: Who to Watch For Prediction #5

Previous Prediction: Who to Watch for Prediction #3

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