Most wearable devices can’t run AI models locally. They’re too power-hungry to run on the device itself, so data is typically sent to the cloud for processing. But new processors may change that.

Ambient Scientific is among the companies betting on analog computing techniques to make battery-powered, on-device AI more feasible. The Santa Clara, Calif.–based company combines digital and analog elements in a chip built for deep learning. Now, it is partnering with India-based tech firm Dimension NXG to make a wearable device using its technology.

On 10 March, the two companies introduced Mai, a wrist-worn, screenless device built for women’s health and safety. The wristband is powered by Ambient Scientific’s GPX10 Pro, which allows it to continuously run AI algorithms for up to two weeks on a single charge. Those algorithms are trained to detect when the wearer is in danger by detecting falls or stress cues.

Dimension NXG has also begun a field trial phase for Mai, shipping them to thousands of users across India. The company plans to have more than 10,000 units available by the end of the year.

Analog Computing in AI Wearables

For AI, “95 percent of the mathematics that runs in these algorithms is just one mathematics. It’s called multiply-accumulate [or] MAC,” says GP Singh, the CEO of Ambient Scientific. These operations are foundational to the matrix multiplication that makes deep learning possible. In general, analog computing is slower and less accurate than digital computing for most calculations—except MAC operation. Analog methods process data in parallel, so they’re more efficient in performing certain complex tasks, like matrix multiplication, compared to digital’s binary logic.

That means analog computing should be a great fit for AI. The problem? At scale, it’s difficult to reliably produce these analog systems, because even microscopic variations during fabrication can significantly alter a circuit’s performance. So, to get the best of the analog and digital worlds, Ambient Scientific converted the most sensitive functions to digital signals.

GPX10 has three main advantages, according to Singh. First, “it is extremely programmable and flexible,” so it can be used for many different applications. Second, the power consumption is very low; even with 10 AI cores, the chip can run on a battery-operated device. And third, it can take input from up to 20 digital sensors simultaneously, making it ideal for wearables or IoT devices.

This version of the device has a limited amount of memory, Singh says, but it’s sufficient for the applications Ambient Scientific is now targeting, including wearables like Mai.

Mai’s Privacy-Focused Safety Features

During sleep, the human brain enters a subconscious mode, but it can still easily wake up if needed. “Sleep mode” in most of today’s devices shuts down systems more completely. “They are not really subconscious,” Singh says. By comparison, Mai continuously consumes microwatts of power to stay alert.

The device continuously monitors for three signals: fall detection, assault recognition, and safeword recognition. Mai is connected to a 5G network, so when one of these signals is detected, an alert is immediately sent to emergency services or a family member with a GPS location.

“Having the safeword recognition always on is extremely important so that the user doesn’t have to push a button on the device or raise their hand, because they might be in situations where they’re not able to do that physically,” says Saharsh Singhania, the head of product marketing at Ambient Scientific.

Some users may be concerned about their privacy while wearing a device that is always listening. But because Mai’s intelligence is on the device itself, most data never leaves the device. It only sends alerts, not voice recordings, when troubling signals are detected, and the rest of the data is thrown out within milliseconds. This means sensitive data isn’t stored by the company, and it significantly reduces the opportunity for attacks from third parties, which often take place when data is sent to and from the device via Wi-Fi or Bluetooth, Singh says.

Reducing False Positives in Wearables

False positives have been one of the main challenges for these types of devices. Too many of them, and the wearable could become the wristband that cried wolf.

Singh says Mai’s algorithms are sophisticated enough to differentiate between, for instance, someone falling and forcefully sitting down. It detects false positives less than once every three days, which Singh says is less frequent than other devices.

Mai is also designed for women’s health. Like other wearables, it can collect data about heart rate and blood oxygen. Dimension NXG is now working with a prominent medical research facility on clinical studies, seeking early indicators of polycystic ovarian syndrome, a hormonal disorder that affects 10 to 13 percent of reproductive-aged women.

The device is first launching in India, but Dimension NXG plans to expand to other Southeast Asian countries. Ambient Scientific, meanwhile, has more capable AI chips on its roadmap, including a 64-core version for use in applications and robotics and drones, as well as a processor for use in data centers.