Planet Arduino

The challenge

Personal safety is a growing concern in a variety of settings: from high-risk jobs where HSE managers must guarantee workers’ security to the increasingly common work and study choices that drive family and friends far apart, sometimes leading to more isolated lives. In all of these situations, having a system capable of sensing and automatically contacting help in case of emergency can not only give people peace of mind, but save lives.

• 

• 

A particularly interesting case – as the world population ages – regards the increasing number of elderly people who are still healthy enough to be independent yet must also accept the fact their bodies are becoming weaker and their bones more fragile. This specific target is more prone to falls, which can result in fractures, head injuries, and other serious accidents that can severely impact the quality of life. Detecting falls early can allow for prompt medical attention and prevent serious consequences. Additionally, detecting falls can help identify underlying health issues or environmental factors that may be contributing to accidents, allowing for appropriate interventions to be put in place to avoid future falls.

A variety of person-down systems and fall detection methods exist, ranging from threshold-based algorithms to traditional machine-learning applications. The biggest challenge they all share is they suffer from high false-positive triggers. In other words, they cause unnecessary alarm and distress to both the seniors and their caregivers, resulting in unwarranted actions. 

Our solution

A tiny but mighty deployment device: Nicla Sense ME

For its project, Aizip selected the Nicla Sense ME: a compact module integrating multiple cutting-edge sensors from Bosch Sensortec, enabling sensor fusion applications directly at the edge. Additionally, the module houses an Arm^® Cortex^®-M4 microcontroller (nRF52832) leveraging Bluetooth^® 4.2. Aizip’s neural network model fits right in with the remaining resources of the microcontroller, thanks to its compact footprint. The result? A small and lightweight device that can be clipped onto one’s belt and worn all day without hassle, able to monitor health parameters and immediately alert assistance in case of fall, with near-zero latency and full respect for privacy.

A more accurate fall detection algorithm

Aizip’s fall detection solution integrates a neural network algorithm with sensor fusion to greatly enhance detection accuracy, while also being lightweight enough it can run in real time on a microcontroller. The neural network within the microcontroller continuously processes sensor readings from the accelerometer (BHI260AP) and the pressure sensor (BMP390). Upon detecting a fall, the device sends an alarm via Bluetooth and activates an on-board LED. In order to minimize frequent false alarms that could significantly affect user experience, the neural network is optimized to differentiate real falls from abrupt movements such as jumping, sprinting, and quickly sitting down. The neural network-based algorithm excels at capturing subtle features in inputs, leading to a substantial reduction in false alarm rates compared to threshold-based approaches or traditional machine learning algorithms.

Typical neural networks offer superior performances but also pose additional challenges, when deploying them onto resource-constrained microcontroller devices, due to the extensive computing and memory resources required. The simultaneous need for Bluetooth connectivity and sensor fusion further compounds this issue. However, Aizip’s proprietary efficient neural network architecture makes this solution stand out because it minimizes resource requirements while maintaining high accuracy. The neural network is quantized to 8bit and deployed onto the microcontroller using Aizip’s automated design tool. The implemented model achieves a 94% fall detection accuracy and a <0.1% false positive rate, all while utilizing less than 3KB of RAM. A perfect fit for the low-consumption Nicla Sense ME!

Solving it with Arduino Pro

Now let’s explore how we could put all of this together and what we would need for deployment both in terms of hardware and software stack. The Arduino Pro ecosystem is the latest generation of Arduino solutions bringing users the simplicity of integration and scalable, secure, professionally supported services.

Hardware requirements

• Arduino Nicla Sense ME
• Single-cell 3.7 V Li-Po or Li-Ion battery
• Jumper wires (for connecting the board and the battery)

Software requirements

• Arduino IDE
• Arm CMSIS-NN Library

Conclusion

When personal safety is a concern, smart wearables that leverage AI can help. And processing the data required to monitor health conditions and prevent falls doesn’t have to come at the expense of comfort or privacy: thanks to extremely efficient models like Aizip’s and compact yet high-performance modules like Arduino Pro’s Nicla Sense ME, you can create a discreet and reliable solution able to immediately call for help when needed (and only when needed).

The post Fall detection system with Nicla Sense ME appeared first on Arduino Blog.

For those aged 65 and over, falls can be one of the most serious health concerns they face either due to lower mobility or decreasing overall coordination. Recognizing this issue, Naveen Kumar set out to produce a wearable fall-detecting device that aims to increase the speed at which this occurs by utilizing a Transformer-based model rather than a more traditional recurrent neural network (RNN) model.

Because this project needed to be both fast and consume only small amounts of current, Kumar went with the new Arduino GIGA R1 WiFi due to its STM32H74XI dual-core Arm CPU, onboard WiFi/Bluetooth®, and ability to interface with a wide variety of sensors. After connecting an ADXL345 three-axis accelerometer, he realized that collecting many hours of samples by hand would be far too time consuming, so instead, he downloaded the SisFall dataset, ran a Python script to parse the sample data into an Edge Impulse-compatible format, and then uploaded the resulting JSON files into a new project. Once completed, he used the API to split each sample into four-second segments and then used the Keras block edit feature to build a reduced-sized Transformer model.

The result after training was a 202KB large model that could accurately determine if a fall occurred 96% of the time. Deployment was then as simple as using the Arduino library feature within a sketch to run an inference and display the result via an LED, though future iterations could leverage the GIGA R1 WiFi’s connectivity to send out alert notifications if an accident is detected. More information can be found here in Kumar’s write-up.

The post This GIGA R1 WiFi-powered wearable detects falls using a Transformer model appeared first on Arduino Blog.

Bone density, strength, and coordination all decrease as we age, and this fact can lead to some serious consequences in the form of slips, falls, and other accidents. In Finland, falling is the most common type of accidental death among those age 65 and over, amounting to around 1,200 per year. But Thomas Vikstrom hopes to decrease this number by detecting falls the moment they occur through the use of the Arduino Nicla Sense ME’s accelerometer together with a K-Way jacket and a smartwatch.

At first, Vikstrom tried to gather and label data for all kinds of activities, including sitting, walking, running, driving, etc., but later realized anomaly detection would be much better suited for this application. After collecting around 80 seconds of data with Edge Impulse Studio, he trained an anomaly detection model to detect when any out-of-the-ordinary events occur. The model was then deployed to the Nicla Sense ME by integrating the inferencing code with a BLE service that outputs a positive value when a fall is detected, as well as illuminating the onboard LED.

To receive this information, Vikstrom added a Bangle.js smartwatch to the system which automatically calls an emergency number if the wearer fails to intervene. For more information about this project, you can check out his Edge Impulse docs page here. Although only a proof of concept, this K-Way demonstrates how tinyML-powered outerwear can be used to detect falls, and together with cellular network devices send for help in case the user is immobile.

The post Detecting falls by embedding ML into clothing appeared first on Arduino Blog.

A dangerous fall can happen to anyone, but they are particularly dangerous among the elderly as that demographic might not have effective ways to get help when needed. Rather than having to purchase an expensive device that costs up to $100 per month to use, Nathaniel F. on Hackster wanted to build a project that harnessed the power of embedded machine learning to detect falls and send an alert. His solution involves the Arduino Nano 33 BLE Sense board, which not only has an integrated accelerometer but also contains Bluetooth Low Energy capabilities that lets the processor communicate with the accompanying mobile app. 

Nathaniel trained his ML model on the SmartFall dataset, which allows the device to respond to a wide variety of falls and ignore non-harmful movements. Once training was completed, he was able to achieve an accuracy of 95%. The Nano 33 BLE Sense samples accelerometer data at 31.25Hz to match the dataset’s frequency, and it makes a prediction every two seconds. If a fall is detected or the built-in emergency button was pressed, the user has 30 seconds to deactivate the alarm, otherwise it sends a BLE message to the phone which in turn sends an SMS message to an emergency contact containing the current location. 

Even though this DIY fall detector works well already, Nathaniel plans on making a custom PCB and extending the battery life for longer use time between charging. You can read more about his design here, and you can view his demonstration video below.

 

The post This wearable device sends an alert whenever it detects a fall appeared first on Arduino Blog.