Building the next generation of e-mobility devices means solving three critical challenges: ensuring rider safety in unpredictable conditions, maximizing battery efficiency and delivering a user experience that drives adoption. Advanced IMU sensors with AI-powered sensor fusion address all three. By processing multi-axis motion data in real-time, these systems enable your devices to detect falls before they occur, adapt power delivery to riding conditions and provide the stability and responsiveness that users expect from premium products. This blog takes a closer look at how IMU sensors, particularly wireless IMUs and wearable IMUs, make e-mobility safer, smarter and more reliable.
What Are IMU Sensors and Why Are They Crucial in e-Mobility?
IMU (Inertial Measurement Unit) sensors are compact devices that measure motion, orientation and gravitational forces. They typically combine accelerometers, gyroscopes and sometimes magnetometers to track movement in three-dimensional space. These sensors are essential in many fields, but their role in e-mobility is becoming increasingly important.
In modern electric vehicles, two-wheelers, robots and micromobility platforms, IMU AI plays a significant role. Edge AI, combined with IMU data, helps machines understand their position, detect anomalies and respond in real-time. Integrating automotive IMU sensors with smart mobility systems allows developers to improve rider safety, enhance navigation and offer performance tracking with a level of precision that GPS alone cannot provide.
Enhancing Rider and Passenger Safety with IMU Technology
Safety is at the heart of all mobility advancements. IMU sensors contribute in several ways:
1. Accident Detection and Emergency Response: Wearable IMU sensors are now being used in smart PPE: helmets, jackets and even smart wristbands to detect sudden impacts or abnormal movements. These sensors that run crash detection algorithms can quickly determine if an accident has occurred and send alerts to emergency contacts or services, potentially saving lives.
2. Stability Control: Whether it’s an e-scooter or an autonomous delivery robot, maintaining balance on varied terrains is critical for the success of many applications. IMU sensors constantly monitor tilts and angles, ensuring that systems maintain balance in real time. IMU sensors, along with other important sensors and sensor fusion, grant vehicles the ability to perform recovery maneuvers and right themselves if they start tipping or swaying.
3. Accident Prevention for Senior Riders: In cities that are actively adopting e-mobility and prioritizing accessibility, safety for elderly and mobility-impaired users is a key design consideration. Wireless IMU systems embedded in smart wheelchairs or scooters can detect irregular movements like a jolt or sudden stop that indicate an accident has occurred. Early alerts help prevent injury and boost user confidence, thus encouraging the adoption of technology-assisted mobility solutions.
Driving Performance Optimization Through Data
IMU technology doesn’t only help with safety, it plays a key role in enhancing automotive performance and increasing the appeal of adopting this technology on a wider basis.
1. Real-Time Motion Tracking: Vehicles powered by IMU sensors can track real-time movement data, including acceleration, rotation and orientation. This data helps fine-tune ride performance, suspension systems and shock absorbers.
For instance, electric bikes equipped with automotive IMU sensors can adjust power output depending on the terrain. On rough surfaces, they may offer extra torque, while on flat roads, they maintain energy efficiency.
2. Maintenance and Predictive Servicing: Through consistent data collection, edge AI algorithms can help identify issues before they become major faults. Sensors pick up on changes in vibration patterns or unusual orientation shifts, often early signs of mechanical wear. Sensor fusion smooths and fuses the data to make sure that it is usable. For example, inertial sensor fusion captures patterns found inside motion data. This can then be layered with edge AI to enable predictive maintenance, reducing downtime in e-scooter fleets and repair costs.
3. Rider Behavior Analysis: By gathering motion data from wearable IMUs, developers can gain valuable insights into how users interact with mobility platforms in real-world conditions. Patterns such as sudden braking, sharp turns or consistent use of specific terrains can reveal important aspects of rider behavior and preferences. These insights enable manufacturers and urban planners to design safer, more responsive and user-centered mobility systems, ultimately improving both automotive performance and the riding experience.
IMUs in Autonomous Navigation
One of the biggest challenges in smart e-mobility is reliable navigation, especially in areas where GPS fails. That’s where IMU sensors step in.
1. GPS-Denied Environments: Tunnels, urban canyons and indoor settings often disrupt satellite signals. Without these signals, vehicles must rely on another source for reliable navigation. IMUs, with help from inertial sensor fusion, can maintain accurate location tracking even when GPS is lost. This makes them essential for last-mile robots, autonomous delivery units and warehouse logistics vehicles.
2. Route Planning and Orientation: IMU data improves path prediction and rerouting accuracy. Combined with visual or lidar inputs, it enables vehicles to adapt to sudden changes in traffic, detours or construction zones.
3. Sensor Fusion for Precision: IMUs alone are powerful, but when combined with other sensors like wheel encoders, GPS and cameras, the results are even more reliable. This sensor fusion approach creates a fuller understanding of the surroundings, ideal for automated systems navigating complex spaces.
Advancing Infrastructure with IMU Data
Beyond vehicles, IMU sensor data can be used to plan and enhance infrastructure.
Inertial sensors provide detailed measurements of motion and vibration, offering insights into both human movement and road conditions. When mounted on vehicles, IMUs record acceleration, braking, turns and vibrations, capturing how vehicles interact with the city’s infrastructure. Aggregated across many vehicles, this data reveals patterns that might otherwise go unnoticed.
For urban planners, IMU readings help map travel behavior and congestion. Frequent sharp decelerations or swerving in specific locations can indicate confusing intersections, high traffic stress or poorly designed pedestrian crossings. By identifying these hot spots, authorities can target improvements such as better signage, adjusted traffic signals or enhanced lighting.
At the same time, IMUs monitor the physical state of the roads themselves. Repeated vibration spikes or irregular acceleration profiles signal pavement wear, potholes or uneven surfaces. With this data, repairs can be prioritized proactively, improving safety and riding quality from bicycles to buses, before small issues escalate into larger problems.
By combining motion data from both vehicles and users, IMUs provide a continuous, data-driven perspective on how people move through cities and how the infrastructure responds, enabling informed decisions that enhance efficiency, safety and comfort.
Defining IMU Intelligence
A decade of experience has allowed us to develop solutions that deliver consistency without adding unnecessary weight or cost. Hardware reliability is a must-have in this context, as it directly affects road safety with regards to human lives.
Effective IMU sensors must capture high-quality, application-specific data as their foundation. Beyond raw data acquisition, they require sophisticated sensor fusion algorithms and edge processing capabilities to transform measurements into actionable intelligence. True intelligence means deriving insights that are not only accurate but contextually relevant to real-world conditions.
Our experience has shown that generic sensor data is insufficient for critical mobility applications. Precision intelligent sensing requires understanding of the operational environment, identifying what matters in each specific context and delivering insights that actively contribute to safer, more efficient e-mobility systems. This context-aware approach is what separates reliable sensing from merely functional sensing.
Real-World Use Cases of IMU Sensors in e-Mobility
Here are a few examples of how IMU technology is applied today:
1. Smart Helmets: Integrated wearable IMUs can detect crashes or falls and automatically trigger alerts that include the user’s GPS location. To make these detections reliable, the IMU’s motion data is processed directly on the device using edge AI algorithms. These algorithms analyze acceleration patterns and orientation changes in real time, allowing the system to distinguish between everyday movements, like bending or sitting, and true man down events that indicate a serious incident.
Solutions such as NeuraSafe demonstrate how edge AI can achieve near-perfect detection accuracy, ensuring that emergency alerts are sent only when they’re genuinely needed and that first responders can act without delay or wasted effort.
2. Unmanned Aerial Vehicles (Drones): Drone IMUs enable precise motion tracking to navigate narrow corridors and urban airways, maintaining stable flight even during GPS signal loss. However, this capability depends on sensors acquiring high-quality data for the sensor fusion algorithms to process effectively. Having reliable sensor fusion algorithms is important because, in practice, flying too low or too fast increases risks to nearby people and jeopardizes mission success if the drone crashes.
3. Electric Skateboards: Wireless IMU technology enables real-time balance adjustments, smoother turns and controlled stopping. As an example, for skaters with disabilities affecting balance, coordination or motor control, this becomes an assistive technology that makes skateboarding accessible where traditional boards would be unsafe or unusable. By integrating IMU sensors onboard, these systems provide riders with greater independence and confidence, allowing them to participate in the sport they love with significantly reduced safety concerns.
4. Ride-Sharing e-Bikes: IMU-based systems help identify misuse, monitor performance and prevent theft by alerting operators when unauthorized motion is detected. Geo-fencing stops users from operating in areas where there is high-pedestrian traffic, contributing to a higher risk of accidental collision. This protects both the users and the fleet from harm. In certain cases, driver monitoring can be used to determine unsafe behavior and shut off a device based on erratic driving patterns that indicate drunk driving or general unsafe driving practices.
A Growing Future for e-Mobility
IMUs have evolved beyond simple motion detection into integral components of sophisticated decision-making platforms. Advances in sensor fusion algorithms, edge computing and machine learning will continue driving their expanded application across the automotive and e-mobility sector.
In the coming years, key developments will include:
Public Transportation Enhancement – Widespread use which will drive demand for passenger tracking and counting systems, improving fleet management and service optimization.
AI-Driven Traffic Integration – Enhanced vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication will enable more reliable, responsive traffic management systems.
Shared Mobility Intelligence – Fleet operators will increasingly deploy IMU-based monitoring to track vehicle usage patterns and rider behavior, optimizing maintenance and safety protocols.
Accessible Mobility Solutions – Advanced IMU-enabled assistance features will provide elderly and disabled users with greater independence through improved stability control and adaptive response systems.
Conclusion
We help companies worldwide to incorporate precision edge AI and inertial sensing into their products. Our commitment to quality, innovation and affordability makes us the preferred choice for those building a smarter future in e-mobility.
