Motion intelligence: Placing people at the heart of the mobility revolution
Life is in constant motion. People move from place to place, from one moment to another, from one stage in their life to the next, changing personalities and needs along the way. When you can understand not just where people are going, but why and how — having high motion intelligence, if you will — you can unlock amazing new possibilities.
Motion data from smartphones has introduced a world of opportunity for companies to create one-on-one relationships with people that are truly personalized and always-on. Analysis of the physical movement of the mobile phone can help detect mobile events that can be translated into meaningful moments, reflecting real-life and real-time context and routines. And all of this can be done while safeguarding consumer privacy through combining first-party business models and advances in edge computing.
According to GSMA Intelligence, there will be over 25 billion connected devices by 2025. While IoT is rapidly becoming a mainstream technology in consumer markets, use cases for IoT are shifting from just connecting devices to addressing specific problems or needs with real solutions.
Let’s examine how enterprises across health, automotive and mobility are putting people at the center by translating motion data into actionable insights to help them better improve people’s lives.
By contextualizing data, healthcare companies are becoming more patient-centric
Wearables and connected health devices provide a treasure trove of data points for healthcare providers. Data such as number of steps taken, heart rate and calories burned from wearables, and nutrition intake, glucose levels or heart rate variability from connected devices can help healthcare providers derive better insights into patients’ health and lifestyles. If they also understand the context of their patients’ behavior, healthcare providers can develop highly personalized treatments for their patients. They can also learn when it’s best to engage individuals to steer them toward healthier outcomes. For example, Dutch medication adherence app MedApp is contextualizing smartphone sensor data to provide personalized smart alarms for medication intake. The app can detect if a consumer is stuck on the subway or driving home and adjust the medication alarm for when he is back home and able to take his medicine.
Another example of where healthcare providers are innovatively using smartphone sensor data to provide one-on-one, real-time patient care is in gait analysis. For patients who recently had back, knee or hip surgery, for example, through sensor data healthcare providers can monitor a patient’s movement post-surgery to see if he is getting back to a normal gait. Instead of taking patients through expensive and exhaustive outpatient care procedures, healthcare providers can inexpensively monitor a patient’s gait through the mobile phone, without the patient ever making a doctor’s appointment.
Car manufacturers are personalizing the in-car experience for drivers
For decades, car technology has traditionally focused on optimizing the vehicle’s internal functions, but attention is now turning to enhance the car’s ability to bring an in-car experience that is customized to fit the needs of drivers and passengers. By connecting smartphones to the car’s dashboard or infotainment system, and using behavioral data and real-time context awareness, car manufacturers can make the driver’s experience more personal, practical and enjoyable. For example, the French car company Peugeot paired AI and smartphone sensor data when it built its Instinct concept vehicle. Peugeot then tailored presets and recommendations based on driver profiles and real-time context. With smartphone sensor data, car companies are creating personalized digital experiences during the car journey, as well as delivering relevant contextual assistance pre- and post-trip.
Enhanced driver coaching for ride-hailing companies
Some of the world’s largest ride-sharing platforms are using algorithms to score the behavior of their drivers based on the sensors in the drivers’ phones. By monitoring the movement of a driver’s mobile device, ride-hailing companies can determine how smoothly, anticipatively and legally their employees drive. Smartphone sensor data can also help them detect when their drivers handle their cellphones while they drive. These insights can better inform ride-hailing companies on how they need to coach their employees to become safer drivers.
Car insurance companies can also contextualize smartphone sensor data to get a more holistic profile of a driver’s behavior and essentially form a “driver DNA” for each of their customers. What are their customers doing while they drive? Are they using their cellphones while driving? Where are drivers coming from and where are they going? Do they spend too much time at work and not get enough sleep? Understanding the entire context of a driver is important for making more accurate predictions about risk profiles, and also allows insurers to coach their customers to become better drivers.
Today’s modern enterprises have no shortage of data to make conclusions about how people go throughout their daily lives, and mobile sensor data is increasingly becoming a more necessary component of this data mix. By contextualizing data from users’ smartphones, companies can get a more holistic view into how people move, which can arm them with the right systems to help make people’s lives easier. Be it through personalized health coaching, customized in-car experiences or driver coaching, sensor data from smartphones is at the heart of bringing more seamless lifestyles to users.
More blog posts
AI-driven crash detection with a human-centric approach
Sentiance wins “Insurtech Product of the Year 2020” award from TU-Automotive Connected Car Insurance
Retail banking: from transactional convenience to a customer-centric lifestyle partner
Improving Driver Safety for every Last Mile Delivery
How will Covid-19 impact the auto insurance industry?
The silver lining of social distancing in Belgium: A contextual analysis