P
pigzippa47
Guest
- Thread Author
- #1
Cordless power tools are evolving from purely performance-driven devices into smart, connected systems.
This blog post was originally published at Infineon’s website. It is reprinted here with the permission of Infineon.
This article highlights how machine learning, edge AI, cameras, and Bluetooth® Low Energy can enable features such as predictive maintenance, adaptive performance, safety monitoring, material detection, user personalization, and over-the-air updates. These technologies will become key differentiators for premium tools over the next 3–5 years, with platforms like Infineon’s PSOC
When was the last time you picked up a power tool and thought about high-tech disruption?
For the average user, power tools can feel like a mature, almost unevolving piece of electronics: better batteries, a little bit more power, or slightly longer runtime, maybe a brushless motor, but fundamentally the same drills, saws, and grinders we have used for decades. Would it be possible this is about to change?
A wave of emerging technologies could silently be starting to transform power tools from electro-mechanical devices into connected, intelligent systems. Advancements in on-board processing, machine learning, and wireless connectivity are converging, enabling tools that can adapt to materials in real time, protect users proactively, and integrate seamlessly into digital and secure applications. The result is a quiet but profound shift: the next generation of power tools will not just work harder or longer–they will work smarter.
Tools are still expected to do the job
Performance in power tool torque, speed, runtime, and durability (especially in premium brands) is still expected. Adding fancy new technology to a tool that does not perform is not the goal. Competition in the market is fierce, top-tier brands can deliver enough power to drive large fasteners, cut dense materials, and survive harsh jobsite conditions. But how to upsell a tool beyond that? This raises a key strategic question: if everyone in the high end can hit the performance benchmarks, how do you stand out? There is opportunity in differentiation.
A high-end tool performs well, but how smart and integrated is the tool? I see this as walking the extra mile and there is a market for that.
If you had a tool that sells for $110 dollars, would you pay 20% more for connectivity to a smart phone app that offers advanced features, firmware updates, logs, predictive maintenance, tech support interface? I would. That gap becomes even smaller when you think about OPE, which tends to cost more. Tools are still expected to be reliable and perform the job, on average, most users purchase a new DIY tool every 3-5 years. You will recover those extra bucks through time. If you are a premium user of contractor, the tool is expected to last long during harsh conditions, but what if this connectivity or machine learning features help you do your job 10% more efficiently. Is it worth to pay a little bit more for the return? My opinion is that emerging technology can be a good differentiator even in premium tools.
Features like adaptive power delivery tuned by machine learning, seamless Bluetooth® Low Energy connectivity for configuration and tracking, secure user profiles, usage analytics, and integration with digital project and asset management systems become a new battleground. Premium vendors might compete in the near term on intelligence, usability, and ecosystem value–not just on raw specs.
The motivation for evolution
The average power tool user is already surrounded by high tech in nearly every other part of daily life: smartphones with facial recognition and AI assistants, cars packed with sensors and advanced driver-assistance systems and very close to self-driving cars at major scale, homes filled with connected gadgets that can be monitored and controlled from anywhere. This constant exposure sets expectations–people now assume that their devices should be smart, connected, and secure by default. When they pick up a power tool that operates like a purely electro mechanical device from decades ago, the gap is obvious. That lived experience creates pressure for power tools to evolve in the same direction: leveraging machine learning to adapt performance and prevent misuse, using connectivity (such as Bluetooth® Low Energy) for configuration, updates, and fleet management, embedding robust security to protect data, access, and safety-critical features and maybe even facial recognition. Why not?. In other words, the “smart device” mindset is no longer optional; it is becoming a baseline requirement for the next generation of power tools.
Machine learning and Bluetooth® Low Energy: two technologies driving change for power tools
Power tools are undergoing a quiet revolution. No longer just motors, gears, and batteries. The latest platforms are adding more sensors, connectivity, and on-board intelligence that fundamentally change how they are used, maintained, and managed. Two example technologies are machine learning (ML) and Bluetooth® Low Energy, which together are turning previously basic tools into smart, adaptive systems.
Machine learning brings data-driven decision-making right into the tool. By analyzing signals from accelerometers, current sensors, temperature probes, and other inputs, ML models can recognize usage patterns, detect anomalies, and predict failures before they occur. This enables features like real-time performance optimization, adaptive speed and torque control, automated safety shutoffs, and predictive maintenance, all running either on the edge (in the tool) or in the cloud.
Bluetooth® Low Energy acts as the communication bridge that connects tools to mobile devices, gateways, and backend systems. With ultra-low power consumption and standardized profiles, Bluetooth® Low Energy provides a practical way to configure tools, update firmware, log usage, and integrate with fleet management platforms. Together, ML, and Bluetooth® Low Energy enable a new generation of connected power tools that can be monitored, controlled, and optimized at scale–offering benefits across productivity, safety, lifecycle cost, and user experience.
Below are some potential use cases for your consideration:
Truck/locker for tools use case
Imagine a scenario where tools are stored in compartments in a truck or an on-site locker system. Access control today is often manual or based on physical keys/badges, which can be lost, shared, or bypassed. With Infineon’s PSOCTM Edge family, a low-power camera and on-device facial recognition could authenticate workers before granting access to specific compartments or tools. This would enable fine-grained control, such as only allowing trained and certified users to check out high-risk tools like chainsaws or concrete cutters, while giving broader access to low-risk tools. The system could log who took which tool, when, and for which job, improving accountability and asset tracking. Over time, the same platform could link into training records: a worker only gets access to a chainsaw after completing the required safety training, with the locker enforcing that policy automatically. This directly improves safety, prevents unauthorized tool use, and reduces theft and loss.
Experienced users make safer decisions and provide an extra layer of safety even for professionals, especially in cluttered or urban environments.
Power tool safety use case (object detection/emergency stop)
By integrating a camera and running object-detection models on PSOCTM Edge, a tool could monitor the area nearby for safety reasons. If a hand, limb, or any forbidden object enters a defined danger zone, the tool could trigger an immediate stop or a rapid brake, adding an intelligent safety layer beyond mechanical safeguards and inertial sensors. The same system could be extended to detect protective gear (e.g., gloves, chaps) or recognize when the saw is being used too close to bystanders. This kind of vision-based safety envelope is a strong fit for edge ML and illustrates how future chainsaws could dramatically reduce the risk of severe injury.
Material detection use case (camera-based classification)
A camera on a saw to detect the material being cut. With PSOCTM Edge, a compact vision model could classify targets as wood, metal, masonry, concrete, or foreign objects (e.g., nails, embedded hardware). The tool could adapt its behavior accordingly, adjusting power delivery, triggering alerts, or stopping altogether if it detects a high-risk object that might damage the blade or cause kickback. For instance, detecting metal plates or rebar in a cut path could prompt a warning before the blade engages, reducing blade damage and safety incidents. Over time, the model could be trained on customer-specific materials or job types, making the saw smarter and more adaptable in real-world scenarios.
Tool configuration and personalization via app
Tool configuration and personalization via Infineon’s AIROCTM Bluetooth® Low Energy and a mobile app turns a power tool or OPE device into a configurable platform tailored to each user, task, or jobsite.
Instead of adding complex buttons, screens, and menus on the tool itself, Bluetooth® Low Energy allows all that complexity to be handled by a phone or tablet. A drill, saw, or can have a simple configuration interface over Bluetooth® Low Energy; the app then provides a rich UI for selecting and tuning parameters. Users can create and save multiple profiles–for example:
- Performance profiles
- Max. RPM/torque limits
- Soft‑start vs. aggressive start
- Acceleration/deceleration curves
- Eco vs. high‑power modes for runtime vs. performance
- Application-specific modes
- Predefined profiles for materials: “softwood framing,” “structural steel,” “PVC,” “concrete,” etc.
- Task-based presets: “drywall screw mode,” “lag bolt mode,” “finish cut,” “rough cut,” “wet grass,” “leaf blowing near gravel,” etc.
- User-based personalization
- Individual operator profiles (novice / expert) with different safety margins
- Region- or company-specific safety settings, like lower speed limits or stricter kickback sensitivity
- Different haptic/LED feedback preferences (vibration on overload, light patterns on nearing thermal limit)
- Accessory-aware settings
- Auto-adjustments when a specific battery, blade, chain, or attachment is installed (detected via ID or manual selection)
- Limiting torque or speed to protect delicate accessories (e.g., small bits or fine blades)
Configuration can also include safety and compliance options: setting mandatory guard detection, lock-out timers, or restricted operating windows (e.g., noise-sensitive hours). Supervisors can “lock” profiles so frontline workers cannot override critical settings, while still allowing limited personalization (like soft-start preference).
Bluetooth® Low Energy makes it easy to deploy and manage these profiles at scale. A fleet manager could:
- Push standard company profiles to all tools on a crew
- Clone settings from one tool to another in seconds
- Export/import profiles between jobsites or contractors
- Restore a tool to a known-good configuration after
Over-the-air firmware and feature updates
Over‑the‑air (OTA) firmware and feature updates over Infineon’s AIROCTM Bluetooth® Low Energy can transform power tools and OPE from fixed products into updatable platforms that can improve over time—without cables, PCs, or trips to a service center.
At a basic level, Bluetooth® Low Energy OTA would let a mobile app or site gateway securely download a new firmware image from the cloud and then stream it into the tool. Once verified (e.g., via cryptographic signatures and checksums), the tool reboots into the new firmware. This enables rapid deployment of bug fixes, security patches, and performance improvements to tools already in the field. For example, if a safety edge case is discovered, or a rare motor-stall condition causes unexpected behavior, the vendor can push a corrective update to all affected tools in days instead of waiting for slow, manual service cycles.
Beyond fixes, Bluetooth® Low Energy OTA is a mechanism for feature delivery and lifecycle enhancement:
- New material profiles and work modes
Add or refine drilling/cutting profiles (e.g., for new building materials), improved kickback detection, or optimized power curves as the vendor learns from field data. - Improved ML models and algorithms
Update on-tool machine learning models for anomaly detection, object recognition, or material classification so the tool gets “smarter” over its lifetime. - Regional or customer-specific variants
Deploy different firmware builds for specific markets (noise/safety regulations) or premium customers (advanced modes, extra telemetry) without changing hardware. - Feature unlocks and trials
Enable paid upgrades, time-limited trials, or feature bundles (e.g., “Pro Safety Pack”) after purchase, using firmware changes to turn capabilities on or off.
Infineon’s PSOC
Edge for ML use cases
Infineon’s PSOCTM Edge is well aligned with power tool ML use cases because it is architected specifically for high performance and low power capabilities at the edge, rather than a generic MPU, this provides cost and power consumption advantages. Devices in the family combine an Arm® Cortex®‑M55 CPU and Arm® Cortex®-M33 with an NNLite or an Ethos‑U55 class NPU for acceleration of neural networks, delivering performance in a small, battery‑friendly footprint.
This lets you run vision and sensor models (object detection, anomaly detection, material classification, gesture/pose, etc.) in real time from an on‑tool camera or sensor cluster without relying on cloud connectivity or high‑power processors. PSOCTM Edge supports common ML toolchains and provides enough on‑chip SRAM and external memory interfaces (e.g., QSPI for XIP flash) to host reasonably large models and buffers for 720p‑class streams or multi‑sensor fusion.
From a system design perspective, PSOCTM Edge integrates the peripherals and security features needed in smart power tools and OPE. High‑speed interfaces such as MIPI CSI‑2 or parallel camera inputs (depending on device), multiple SPI/I²C/UART blocks, and timers/PWM units simplify connection to image sensors, motor drivers/inverters, battery management ICs, and IMUs / current sensors.
Hardware security blocks support secure boot, cryptographic acceleration (AES, ECC, SHA), and secure key storage, enabling authenticated OTA firmware and ML model updates via Bluetooth® Low Energy or Wi‑Fi, plus protection against cloning and tampering.
Low‑power modes, programmable wake sources (GPIO, timer, sensor events), and integrated power management help stay within tight battery budgets while still supporting always‑on sensing (e.g., person/hand‑near detection) in the sub‑mW range. Combined with Bluetooth® Low Energy/Bluetooth® Low Energy Mesh or other radio options in the broader Infineon ecosystem, PSOC
Infineon’s AIROCTM for Bluetooth® Low Energy use cases
Infineon’s AIROC
The device is built around an Arm® Cortex®‑M33 core (up to 96 MHz) with TrustZone® and DSP extensions, the AIROC
Bluetooth radio is optimized for high sensitivity and long range at low current consumption, supporting sustained connections and periodic advertising that are essential in noisy industrial and outdoor environments such as a factory or a wide area jobsite location.
For power tools and OPE, CYW20829’s feature set directly enables many of the emerging connected use cases. Its Bluetooth® Low Energy 5.x long‑range and high‑throughput modes support reliable tool‑to‑phone or tool‑to‑gateway links for configuration, telemetry, and streaming diagnostic data. Integrated hardware security—true random number generator, symmetric/asymmetric crypto accelerators, secure boot, key storage—allows secure provisioning, encrypted communication, and authenticated OTA updates, which is critical when tools control high‑power actuators or store sensitive usage/fleet data. Rich peripheral support (multiple SPI/I²C/UART, PWM/timers, ADC inputs, GPIO with wake‑from‑sleep) makes it straight forward to tie into battery gauges, motor controllers, IMUs, Hall sensors, and trigger/guard switches. This lets AIROC
When will this be adopted?
The shift is already underway and will accelerate over the next 3–5 years.
Today, you can see early signs in high‑end tools: basic connectivity via Bluetooth® Low Energy, simple apps for configuration, some sensor‑based safety features, and first experiments with vision (inspection cameras) or basic ML (pattern/anomaly detection). This will expand into more premium and specialty tools—inspection systems, chainsaws, saws, OPE—where added safety and intelligence justify the cost of cameras, ML SoCs like PSOCTM Edge, and robust Bluetooth® Low Energy connectivity with AIROC
Beyond that, in the 5 year horizon, smart, connected, ML‑enabled capabilities might become standard expectations in many professional tools, especially in segments where safety, documentation, and productivity are critical. We are living this transition, so next time you think or use a power tool, dream about its evolution and the exciting functionality that is yet to come.
Putting it all together
Power tools and outdoor power equipment are clearly entering a new phase where differentiation is no longer just volts, amps, and torque, but intelligence, connectivity, and safety. Machine learning at the edge enables smarter behaviors—material detection, anomaly and defect recognition, predictive maintenance, augmented reality guidance, and real‑time safety interventions. Bluetooth Low Energy connectivity ties those tools into the broader digital workflow: configuration and personalization via app, secure lock/unlock, usage logging, asset tracking, and OTA updates that keep tools improving long after they leave the factory.
Infineon’s PSOCTM Edge and AIROCTM CYW20829 provide a complementary platform to build this next generation of smart tools. PSOCTM Edge delivers the on‑device ML performance, and hardware security needed for vision and advanced safety features directly in the tool. AIROC
The post Powering Up Tool Innovation: Emerging Technologies in Cordless Power Tools appeared first on Edge AI and Vision Alliance.