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Assessing technologies for a social distancing wearable

2021-02-26 20:18 Embedded.com Nitya Verma

Social distancing is a cornerstone of COVID-19 mitigation; it continues to play a vital role in reducing the risk of virus exposure and spread. While world health authorities have established that 6 feet (2 meters) is a safe distance, designing devices to assist consumers with social distance awareness and alerts has proven challenging because their core functionality relies on accurate, low-latency distance measurements.

In a recent collaboration, Altran worked together with semiconductor company Renesas to develop an intelligent wearable device/platform and prototype a social distancing wristband based on ultra-wideband (UWB) technology. The wristband alerts the wearer when a second device is detected within a user-specified “safe” distance. This article shares insights from phase one of that project: the process of evaluating wireless protocols to meet requirements for accurate distance measurement while keeping other key platform requirements, such as power efficiency, size and user experience, in balance.

A small device with a large list of requirements

In this project, our goal was to create an embedded social distancing platform suitable for wearable applications that leveraged Renesas IC technologies. As proof of concept, a wristband prototype based on this platform was also designed and manufactured in low volume to demonstrate the functionality (monitoring and alerts) and user experience in a social distancing use case (Figure 1).


Figure 1. A wristband prototype alerts the wearer when a second device is detected within a user-specified safe distance. (Source: Altran)

A wearable form factor dictated the need for one or more wireless technologies, the choice of which centered around a few basic requirements.

  • Accurate distance measurement – for accurate alerts and no false alerts. By far, for our use case, the most important criterion for choosing a wireless protocol is its ability to measure distance with a level of accuracy capable of distinguishing between safe and unsafe distances. Measurement accuracy is also key to eliminating (or severely reducing) the number of false alerts caused by imprecise distance measurement. Receiving alerts that may or may not equate to unsafe distancing makes it challenging for users to decipher real vs. false threats.
  • The impact of the physical environment. The wireless protocol should be minimally impacted by the physical environment of typical use scenarios. The device, in other words, needs to be capable of delivering accurate and repeatable measurements whether it’s used indoor or outdoor, in line of sight (LOS) or non-LOS (NLOS) situations, and in dynamic environments, such as those with many moving objects or changing LOS.
  • Low latency. To be effective, response time between threat detection and user alert must be fast enough that the user has time to take preventative action and/or needed precautions.
  • Form factor. In a wearable device, wireless technology must be lightweight and small.
  • Power efficiency. Wearables are battery operated, but detection – of an object, a person, a signal, etc. – typically involves sensors, components not known for their power efficiency. For our use case, it was crucial to design a wireless solution with exceptional power efficiency, in all operating modes, to deliver expected battery life between charges.
  • Scalability. A social distancing use case, by definition, involves multiple people and often crowds, so the wireless solution must be able to provide reliable and accurate distance measurement for multiple simultaneous targets.

In general, each wireless technology supports distance and location measurement using some combination signal capture (using time-based, angular position, or received signal methods) and positioning techniques (using triangulation or trilateration methods) (Figure 2).


Figure 2: Typical distance/location measurement technique. (Source: Altran)

Evaluating wireless technologies

We evaluated several commercially available wireless protocols to assess how well they could meet our requirements for a social distancing wearable. Our candidates included Wi-Fi, cellular, Bluetooth Low Energy (BLE) and ultra-wideband (UWB). In general, the known distance/ positional accuracy specifications of each eliminated many protocols (Figure 3), but there are merits worth noting here.


Figure 3. Distance measurement accuracy of typical wireless technologies. (Source: Altran, using published references [1])

Wi-Fi

We looked at Wi-Fi first, simply because of its ubiquity. Its wide deployment in indoor environments made it a promising solution for the social distancing use case inside buildings, particularly in complex structures such as airports, alleys and parking garages, or underground locations where GPS and other satellite technologies may not be available or provide low accuracy.

Pros: Due to the widespread adoption of Wi-Fi and convenience of setting up Wi-Fi networks, solutions could be deployed quickly for user positioning with very low cost and effort. In addition, with recent advances in Wi-Fi-based indoor positioning, Wi-Fi can provide reliable and more precise location services (than older Wi-Fi technology) suitable for some social distancing applications.

How it works: In a Wi-Fi system, a wireless transmitter, known as a wireless access point (AP), is required to transmit radio signals to communicate with user devices in its coverage area. The most common and easiest way to support indoor positioning is to calculate the user’s location based on the received signal strength indicator (RSSI) of signals from the user device. RSSI accuracy is in the range of 10+ meters, reduced to 1-3 meters approx. 75-85% of the time when utilizing newer Wi-Fi round-trip time (RTT) technology.

Summary: With the current advances of Wi-Fi, such as RTT, the accuracy of localization systems has significantly improved, resulting in its adoption for many indoor positioning applications. But distance accuracy down to 1 meter is still insufficient for our social distancing use case. In addition, Wi-Fi may not be effective in dynamic and complicated indoor environments due to the effects of NLOS environments, where signals can be scattered by obstacle shadows or people.

Wi-Fi-based technology is also used mainly for indoor and indoor-adjacent environments since it requires several APs for localization which may not provide seamless transitions in indoor-outdoor environments or be feasible in outdoor environments. Wi-Fi APs also require additional infrastructure such as power and protection from the elements, making them more complex to deploy.

BLE

With the explosive growth of Bluetooth-enabled devices in both indoor and outdoor environments, we also considered BLE technology for our solution.

Pros: BLE is used for short-range wireless communications (2.4 to 2.485 GHz); and its localization technology has several advantages when compared to Wi-Fi. BLE signals have a higher sample rate (i.e., 0.25 Hz to 2 Hz), providing more data from which to estimate distance. BLE technology is also more power efficient, thus more suitable for wearable devices. And BLE signals can be obtained from most smart devices, while Wi-Fi signals can be obtained from only APs. Finally, BLE beacons are capable of being battery powered, and thus are more flexible and easier to deploy than Wi-Fi access points.

How it works: Bluetooth-based localization is considered a practical approach in indoor and indoor-adjacent (outdoor patios, decks, etc.) environments. Indoor localization schemes collect RSSI measurements to detect the user’s location by using the triangulation mechanism with data from other Bluetooth devices.

Even though BLE-based indoor localization can achieve better performance than similar Wi-Fi localization systems, BLE technology is strongly affected by fast fading and interference leading to low distance accuracy when detecting another device. Accuracy is also strongly impacted by BLE advertising channels, human movements, and human obstacles. Methods proposed to improve the accuracy have achieved results down to 2 meters.

Summary: Promising for some social distancing applications, Bluetooth technology did not offer the consistency and accuracy of distance measurement for our social distancing wearable. Combining Bluetooth and Wi-Fi technologies was also explored, but this also did not result in the needed accuracy.

Cellular

The cellular network infrastructure being widely deployed today can be used to help locate a person (or more accurately, an active SIM- or E-SIM-enabled smart device) within an outdoor environment. Although cellular connectivity is available within indoor environments, it does not currently produce accurate, reliable or fast enough measurements for our use case. Social distancing is relevant in both indoor and outdoor settings, so our discussion of cellular localization continues focused on outdoor applications.

Over the past few years, we have seen tremendous technological growth in cellular technology, some of which makes it a key candidate for use in location positioning applications. With current cellular networks supporting assisted GPS (A-GPS), enhanced Cell ID (E-CID) and observed time distance of arrival (OTDOA), cellular’s location accuracy has significantly improved.

Pros: One of the biggest advantages of cellular-based distance measurement is that it doesn’t require additional hardware infrastructure; it can operate on existing networks. In addition, most of the global population owns at least one cellular-equipped smart device, so deployment requires only a mobile app and some data processing capacity in the network.

How it works: In outdoor environments, cellular localization techniques use the algorithms mentioned above, namely A-GPS, E-CID and OTDOA. Here, E-CID enhances CID accuracy by adding reference data such as RSS levels and RTT information which is used to triangulate and calculate location coordinates. E-CID is also capable of using angle-of-arrival (AoA) information to improve the overall accuracy. Through these techniques, current LTE-based cellular protocols (3/4G) are capable of distance measurement accuracy in outdoor settings down to a range of 5-10 meters. Adequate if you lose your phone, but not accurate enough for our use case.

Many telcos worldwide are actively deploying new 5G cellular networks, and 5G has performance characteristics that could make it an excellent candidate for next-generation social distancing platforms. Further testing for our use case will bear this out, but given the state of 5G deployment, it was not considered for our project.

5G includes key technologies such as mmWave communications, device-to-device (D2D) communications and ultra-dense networks (UDNs), which contribute to its capacity for high-precision localization. Positioning techniques exploiting the mmWave communications are based on validation of triangulation measurements and angle of differences of arrival (ADOA). Simulations show that triangulate-validate and ADOA methods can achieve sub-meter accuracy with a probability of 85% and 70%, respectively, in an indoor, 18 x 16 m area [2]. Localization accuracy can be further improved by implementing Kalman filtering algorithms.

Next-generation 5G technologies will also enable directional or linear array antennas, which will help make cellular-based positioning techniques viable for indoor applications as well. Here, the basic principles of AoA and time of arrival (ToA) are used for location measurement.

Summary: Suitable for outdoor environments where cell network infrastructure is fully deployed, existing 3/4G cellular protocols can only deliver down to 10-meter distance accuracy, unsuitable for our use case. While future generations of 5G are on track to achieve sub-meter distance accuracy – possibly lower with new techniques – deployment coverage is not sufficient at this time to make 5G solutions a viable choice for our need. And 5G’s suitability for indoor localization is still untested.

UWB

Unlike its Bluetooth and Wi-Fi counterparts, UWB operates at a broad spectrum of GHz frequencies, from 3.1 to 10.6 GHz. While UWB isn’t as widely deployed as the other protocols, it has some unique properties that made it an excellent candidate for our social distancing project as well as future indoor positioning use cases.

Pros: UWB can be used to capture highly accurate spatial and directional data and can sustain measurement accuracy at the centimeter level in short to medium distance ranges. UWB measurement accuracy is capable of distance accuracy down to 5-10 cm depending on the use case. Due to its unique characteristics such as high time-domain resolution, immunity of multipath, low-cost implementation, low power consumption, good penetration and wide bandwidth UWB signals (at least 500 MHz as specified by FCC), the impulse radio UWB technology has the ability to generate very short-duration Gaussian pulses in time domain, which enables some advantages when compared with other wireless RF technologies. Its wide bandwidth also gives it comparatively better immunity to multipath propagation and narrowband interferences prevalent in other communication technologies, since these types of interferences only affect part of the spectrum.

UWB has good penetration in solid materials, such as walls and other structures, so it can perform more consistently in NLOS environments. And a key advantage for our small form factor design, UWB allowed us to use smaller antennas due to increase in operating frequency and the RF circuitry was simpler, even though data transfer rates are higher.

How it works: In UWB communication, ultrashort pulses are used to communicate the data, which permits high-precision estimation of a two-way distance using the duration or time of flight (TOF) for the signals. The higher the spectral density provides more robustness in multi-path environments and hence more accurate ranging (distance measurement) capabilities.

As part of our UWB evaluation, we were provided with a UWB low-rate pulse (LRP) chipset from Renesas. LRP’s main advantage is down to 10 times lower power consumption than other standard UWB solutions, and hence was an ideal fit for our battery-operated wearable. For example, in transmit mode, typical power consumption for UWB high-rate pulse (HRP) ranges from 100 – 120mA, where UWB LRP typically draws 10-20mA. LRP standard based devices are not normally used for distance ranging applications, but the latest standard IEEE 802.15.4z enables them to operate in ultra-low power consumption mode while enabling secure ranging capabilities using round-trip TOF mechanisms we used in distance calculations.

In this first phase of our project, we typically measured UWB LRP’s distance accuracy within 20-30 cm. For clear LOS environments, closer to 20 cm; and for NLOS environments, closer to 30 cm. In next project phase, both distance accuracy and reliability will be tuned further to achieve closer to the needed 10 cm.

When compared to BLE and Wi-Fi, UWB works on short-burst impulse radio from Tx to Rx. In combination with its wide bandwidth, this reduces latency down to sub-ms since no decoding or modulation is required.

Summary: Based on an evaluation of key factors such as distance measurement accuracy, reliability, form factor/size, performance in the typical deployment environment, latency, low power consumption, scalability and reduced sensitivity to interference, we concluded that UWB LRP – leveraged in the new chipset from Renesas – was the best wireless technology for accurate distance measurement our social distancing project.

We finalized the social distancing platform utilizing both BLE and UWB in combination (Figure 4). This gave us the advantages of UWB’s high-precision distance measurement and consistency as well as the power efficiencies of BLE for always-on proximity detection when sensing a device within the local environment. In our application, BLE also supports pushing the historical alert data and actual distance measurements to a mobile app.


Figure 4: The final POC platform uses a combination of BLE and UWB LRP for optimal power utilization. (Source: Altran)

A clear choice for a social distancing wristband

Social distancing and mask wearing remain humanity’s first line of defense against the spread of COVID-19 and other diseases spread through contact or airborne transmission. In this project, Altran and Renesas teamed up to develop an embedded platform for a social distancing use case using a Renesas MCU and UWB LRP chips. While this project included design and (small-volume) manufacturing of a wristband prototype, the platform itself can be easily be adapted to enable social distancing (and contact tracing), as well as other indoor and outdoor location/position-based functionality in many types and form factors of IoT products where distance and location accuracy are essential. The option of using UWB LRP chips further extends the range of use cases to include those where power efficiency is critical.

References

[1] Wireless protocol distance accuracy data:

[2] Simulation results

Nitya Verma is a Director of Embedded Solutions at Altran, part of the Capgemini Group. He is responsible for architecting many types of embedded products, from IoT to electronics, healthcare, industrial, and automotive applications, and leads product innovation for wearables. He also collaborates with Altran’s Research & Innovation team to provide emerging technology strategies and solutions across 5G, edge, wearables, IoT, and automotive domains. He has deep experience in deploying embedded wireless technologies to drive growth across use cases and industries.

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Top 10 tips to accelerate software defects resolution

2021-02-24 19:04 Embedded.com Nitin Dahad

“When it comes to software quality and reliability, prevention is always better than cure.” So said Professor Roberto V. Zicari, editor of data management resource portal ODBMS.org and professor of database and information systems at Goethe University Frankfurt.

However, commercial pressure often means software development teams have to make trade-offs between code quality and pressure to ship new features. “No matter what we do, bugs always end up slipping in and being deployed into the field. So, what do you do when bugs do happen? Just as with our own health, investing in prevention is the right thing to do; but we will always need the hospitals. We need cure, just as much as prevention.”

Based on this premise, he and Greg Law, founder of software failure replay firm Undo, co-authored and released an e-book at the beginning of this year entitled “10 Tips to Accelerate Time to Resolution of Software Defects”. Based on interviews with senior engineers building enterprise software systems to find out what they do when things go wrong, the book explore how they measure and reduce mean time to resolution (MTTR) when bug reports come in, and how they go about reducing the average cycle time it takes to resolve bugs.

This article highlights some of the key points raised in a recent panel discussion to launch the book and understand the issues raised.

But before that here’s a sampling of the key findings:

  • “We always have at least one, maybe two, branches that are ready to release. If something does come up, we want to be ready for it. We’re essentially always ready to release.” Bryan Bowyer, director of engineering, Mentor (a Siemens business)
  • “We have a zero-tolerance policy against defects. So, we never end up with a backlog of issues building up. That, in itself, saves us a lot of time and effort. When a defect creeps in, we’re pretty sure it’s a new bug we haven’t seen before and not an intermittent failure […] we haven’t previously dealt with.” Roisin McMahon, engineering director, Renesas Electronics Europe
  • “Our robust continuous delivery pipeline allows us to develop a fix […] and minimize our mean time to resolution, which is a key metric we use to track the progress of our DevOps journey.” Ken Dickinson, VP of enterprise quality, SAS.

The panel

Hosted by Professor Zicari, the panel consisted of:

  • Greg Law, Founder/CTO at Undo
  • Snehal Khandkar, former senior engineering manager at Rubrik (now at Facebook)
  • Haricharan Ramachandra, senior director of engineering, Salesforce
  • Ken Dickinson, VP of enterprise quality, SAS

Prevention is better than cure

Is prevention always better than cure? The question raised arguments both ways.

Dickinson: You are assuming that you can prevent all the degradations that can happen to your system. Honestly, I don’t think that you can. I think that you have to account for entropy, you have to account for chaos. No matter how beautiful your testing suite is, there will always be things you cannot account for. You have to invest in both: robust testing within your delivery pipeline and your ability to recover. How quickly can you solve a problem that’s detected in production? That’s why mean time to resolution is important. You can’t predict everything, so how quickly can you recover?

Law: Humans are really bad at writing software. Engineering is always about trade-offs. How early can you get bugs out? And how much do you invest in that? The cost of a bug in an airplane is catastrophic (costs hundreds of lives and can come close to costing the company), compared to the cost of a game or an app where you won’t invest anything like as much in prevention.

Ramachandra: ‘Every feature is a bug in a tuxedo’. Every line of code we add increases the probability of a new bug being formed. That’s why building for failures is important. There are lots of causes of failures, software failures, network failures, hardware failures. Google actually pioneered this concept of designing for failure. There’s a famous paper by Jeff Dean that explains how they designed from the ground up assuming that their hardware will fail. Resilience in engineering is an important concept.

Automated tests: do they work?

Dickinson: Test automation is terrible at finding bugs. But it’s really good at preventing regressions and freeing up time from some of my creative humans to go find those bugs and think of those things that automation does not cover. Automated tests are still scripted tests. They do not discover what you haven’t thought of. They can only validate what you have already thought of. If you install an adequate layer of automated testing, it frees up hours of your week for your humans to go after what you can’t easily reproduce with automation and what you haven’t thought of yet. Where I’ve seen the most value in automation is in what can humans do when they have that as a tool at their disposal.

Metrics: how do you use them in practice?

What do terminologies like mean time to toot cause and mean time to resolve mean? How do you use them in practice?

Ramachandra: These metrics relate to how we’re handling things when stuff happens in production.

  • How long does it take to figure what is happening – that’s mean time to root cause. From the time we acknowledge to the point we figure out what’s the problem.
  • When do we fix it – that’s mean time to resolve.
  • And before all of that, we also have mean time to acknowledge: is it my problem or your problem? Sometimes it takes a long time for teams to acknowledge that it is somebody’s problem.

The reason why we break it down this way is to understand where the bottlenecks are and help us build processes around these measures. If we don’t measure it, we can’t fix it.

Law: The above describes state of the art best practices. My experience is that most software organizations, even large companies with greater resources are not at that level yet. At the most basic level – and this always makes me wince a bit – is where people have ‘open defect count’ as a primary KPI on how they are doing. If I go on a big testing spree, and I uncover 500 bugs and I put them in my bug tracking system, my software hasn’t got worse. I just know a little bit more about the ugly truth. Going back a few years, I’ve even seen a resistance to filing a bug because that’s going to make my KPI worse. If you’re going to measure that kind of thing, it’d be much more useful to measure ‘closed rate’ and the age of defects in the system. One of the insightful points that surfaced in one the interviews I did is this: the older a bug is languishing in your bug tracking system and you haven’t touched it for a long time, the harder it is to reproduce.

Issues with automated tests

Dickinson: We have a quarantine policy on automated tests. It’s different with every team, but for example if a test fails 3 times within 2 weeks, it’s a flaky test; that test gets pulled out of the deployment pipeline and it is continuously executed – just not gating anyway – and that test has to prove stability before it can be reintroduced into the pipeline. Stability matters.

Law: If everybody just ignored these flaky tests when they go off, it’s like having a smoke alarm that’s always sounding and people start ignoring things. But you do need a strategy for dealing with them. It is a smoke alarm: “I’m smelling smoke in the codebase”. It’s easy to dismiss but it can be costly. One of things we’ve seen our customers do very effectively is quarantine those flaky tests and run them again and again to get the information they need in order to root cause them – whether that’s running them under recording with Software Failure Replay or whatever means you have available. You need to get it out of the pipeline but not ignore it.

Ramachandra: With reference to flaky tests – or transient failures – one of the challenges that most software engineers in our organization face when it comes to quality is separating the signals from the noise. There’s so much noise and not enough scrutiny on transient failures. Why are these tests flaky? It’s not just about the test code. It might be application behavior or the test environment. Often our test environment is different from the production environment. We cannot think of all the scenarios that can happen in production.

Khandkar: Automated tests safeguard you against regressions; they are testing against the bugs you already know of. They are not helping you find new bugs. At Rubrik, we found it valuable to introduce chaos in our large-scale systems. If you have a large-scale test setup, don’t execute it to plan; add some chaos to it, some unexpected behavior and see how your system responds to it. That was most effective in finding out new bugs.

Reality vs lab testing

Zicari: We do all the tests in the lab and it works, and the same software in production might not work in the way I was thinking. Any of us who are patients going to hospital, and there’s software used in the hospital for doing something serious. How would I feel about it if I heard that they released the software, but it might not work as expected? What is your reaction to people like me who will think ‘gosh, this is scary!’?

Dickinson: The cost of a failure is directly influenced by how quickly we can recover from it. If we introduce a bug, but we can detect it and resolve it – oftentimes before the customer notices it – then the cost of failure is in the basement; so we can afford to be more innovative, and more aggressive in the changes that we introduce. If we’re operating in a market where it’s not the case, then we ratchet back the amount of aggressiveness.

Khandkar: The cost of a bug is very different if you’re looking at a failure in a hospital software or software that goes in an airplane versus a gaming app. If I’m an airplane software writer, I will put in a lot more safeguards and checks; maybe not so much if I’m on the other hand of the spectrum.

Law: You’re right to be scared. You should be terrified. One of the earlier cases of software bugs killing people, there was a machine in the hospital giving radiation treatment for cancer. They tested it and the software worked fine. Once deployed in production, the operators started punching the keys to control the dosage quicker and quicker the more experienced the operators became; they started doing it too quickly and there was some integer overflow and the patient got fried and got killed by the device sending hundred times too much radiation into the patient. The testing environment turned out to be different from the reality in an unexpected way.

“The vast majority of software in the world is not really understood by anybody.”

Indeed.

The full hour-long panel discussion can be viewed here.

The e-book, 10 Tips to Accelerate Time to Resolution of Software Defects, can be accessed here.

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Add voice on a microcontroller without having to code

2021-02-24 11:56 Embedded.com Nitin Dahad

Picovoice, a Canadian startup, has launched what it says is the first platform which lets you add a voice interface on your microcontroller without having to write a single line of code.

Its new Shepherd platform, together with its Picovoice console, streamlines adding voice artificial intelligence (AI) onto microcontrollers, enabling the creation of voice experiences similar to Alexa, running entirely on-device and without requiring internet connectivity. Edge voice interfaces built with Picovoice are private, reliable, zero-latency, and cost-effective, distinguishing them from cloud-based alternatives.

Voice models can be created within a users browser to target onto various platforms (Image: Picovoice)

With small low-power and cost-effective microcontrollers already deployed in billions of devices, the ability to bring voice AI to microcontrollers unlocks numerous potential applications that might otherwise be considered infeasible due to additional engineering costs. This is because the technical complexities and specialized skills needed to train and deploy voice AI intro microcontrollers have limited their wide adoption for speech recognition. Picovoice said that today, only a handful of tech giants have access to this technology.

The company’s Shepherd platform simplifies a process that previously would have taken months of R&D by teams of scientists and engineers; it can now be undertaken by a non-technical individual in under an hour. This significantly reduces risk and time to market. The no-code aspect of Shepherd empowers developers, product owners, and designers to create voice interfaces that run entirely on a power-efficient and bargain-priced microcontroller. No coding or machine learning expertise is required.

Picovoice on ST board
An example of adding a voice interface on an STM32F469, a development board featuring an Arm Cortex-M4 microcontroller. (Image: Picovoice)
Picovoice upload_firmware_frame
Once the board is connected using a Mini-USB Type-A cable, the Picovoice firmware can be uploaded (Image: Picovoice)

Customers can create voice models within their browsers instantly using Picovoice Console. Once the models are trained, they can be downloaded and loaded onto a microcontroller using Picovoice Shepherd, without any embedded expertise. The process takes only minutes, end-to-end. The company’s web site enables free sign-up to train voice models tailored for your own use case. Using its documentation, Shepherd can be installed on a desktop computer, and voicecan be added using supported development boards from major electronics retailers.

Design, train and test in web browser

Picovoice Console is the company’s platform for designing, training, and testing voice interfaces instantly on an internet web browser, with no machine learning skills required. A user simply describes what is needed in plain text and export trained models. The models run entirely on-device using the Picovoice SDK on these platforms:

  • Embedded platforms such as Raspberry Pi and BeagleBone
  • Android and iOS
  • Modern web browsers
  • Linux (x86_64), macOS (x86_64), and Windows (x86_64)
  • Microprocessors such as ARM Cortex-A
  • Microcontrollers such as ARM Cortex-M

Picovoice Shepherd is free. Picovoice Console is free for personal use (e.g. students, researchers, and tinkerers) and offers a 30-day free trial for enterprise users. The enterprise accounts start from USD 400 per month, charged per annum. Shepherd supports popular Arm Cortex-M-based microcontrollers from ST and NXP, with additional support on the way.

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