Powering up tool Innovation: Emerging technologies in Cordless Power Tools
August 26, 2026
Sponsored Blog
Power tools have long evolved through incremental improvements – more torque, longer runtime, and better batteries. While these advances remain essential there is opportunity to differentiate premium products in competitive professional markets. A new wave of innovation is emerging, driven by edge machine learning (ML) and Bluetooth® Low Energy (BLE), transforming power tools from purely electromechanical devices into intelligent, connected systems.
In many professional applications, baseline performance is already expected. Contractors and fleet operators increasingly look beyond raw specifications toward improvements in safety, productivity, and lifecycle efficiency. This shift creates an opportunity for embedded technologies to redefine how tools operate, interact, and evolve over time.
Machine learning for power tools
Edge ML enables power tools to interpret sensor data – such as current, vibration, temperature, or even vision inputs – and respond in real time. Rather than operating with fixed parameters, tools can dynamically adjust behavior based on the task or environment.
Typical use cases include:
- Adaptive torque and speed control based on material or load
- Anomaly detection to identify stalls, overheating, or abnormal vibration
- Predictive maintenance indicators, reducing downtime and service costs
- Enhanced safety mechanisms, such as intelligent shutoff or braking
Running ML models directly on-device minimizes latency, avoids reliance on cloud connectivity, and is critical for maintaining tight power budgets in battery-operated tools.
BLE as the Connectivity Backbone
BLE provides the low-power communication layer that links tools to mobile devices, gateways, and backend systems. Through a smartphone or tablet, users can configure tools, select application-specific profiles, and access diagnostics without adding complexity to the tool itself.
From a lifecycle perspective, over-the-air (OTA) updates are particularly impactful. Firmware improvements, security updates, and even new features can be deployed after a tool is in the field. Combined with ML, this enables tools to improve over time, shifting from static products to continuously evolving platforms.
Emerging Use Cases in the Field
Several high-value applications are accelerating adoption of intelligent tool architectures:
- Vision-based safety systems that detect hands or bystanders in hazardous zones and trigger rapid braking
- Material detection, allowing tools to adapt when encountering metal, masonry, or embedded objects
- Secured access control, facilitating only trained personnel operate high-risk equipment
- App-based configuration, enabling task-specific performance settings without complex hardware interfaces
These capabilities not only improve safety outcomes but also enhance productivity and asset management – key priorities for professional users.
Design Considerations for Embedded Engineers
Enabling these features requires embedded platforms that balance compute performance, power efficiency, and security. Edge-focused MCUs with ML acceleration can handle sensor data processing and vision workloads, while dedicated BLE solutions provide reliable connectivity and secured communication.
Security is a foundational requirement. Features such as secured boot, hardware cryptography, and protected key storage are essential to enable safe operation, especially when tools control high-power actuators and manage sensitive usage data.
Integrated platforms like Infineon’s PSOC™ Edge and AIROC™ BLE families illustrate how combining ML capabilities, connectivity, and security into a cohesive architecture can simplify development while enabling scalable smart-tool designs.
Looking Ahead
Connected features are already appearing in premium tools, and adoption is expected to accelerate over the next three to five years – particularly in safety-critical applications such as chainsaws and cut-off saws. As expectations shift, intelligence and connectivity are likely to become standard, not optional.
Conclusion
Power tools are entering a new phase where differentiation is defined not only by performance, but by awareness, adaptability, and integration. Edge ML and BLE are key enablers of this transformation, unlocking safer operation, improved productivity, and devices that evolve throughout their lifecycle. For embedded engineers, this represents a compelling opportunity to shape the next generation of intelligent tools.
Please visit our website for more information and resources: https://www.infineon.com/powertools.
