Semtech’s LoRa Technology Help Saves Alzheimer Patients in Real-Time

Semtech’s LoRa Technology Help Saves Alzheimer Patients in Real-Time

Semtech’s LoRa Technology Help Saves Alzheimer Patients in Real-Time

The police in Korea plan to give Lineable’s Silver, a wearable Internet of Things (IoT) device, for free to actively locate Alzheimer patients.

Semtech Corporation announced that Lineable, a Seoul-based startup manufacturer of GPS trackers, has integrated LoRa® devices and wireless radio frequency technology (LoRa Technology) in its new wearable safety device, Silver.

Lineable’s Silver device, co-developed by the National Police Agency of Korea, SK Telecom and SK Hynix, is specifically designed for patients with Alzheimer’s. Through a hybrid GPS system, caregivers are notified when patients leave the house or out of the designated safe zone.

Many patients are not constantly monitored by a supervisor, and in Korea, about 10,000 Alzheimer patients go missing each year. Silver is currently being used by the police in Korea and the police plan to distribute 3,000 devices each year to Alzheimer patients, free of charge. During its first month of service in October 2017, the Silver device helped save six patients and in three months, it helped save 20 more patients.

“The Lineable LoRa-based device provides a universal solution for tracking Alzheimer patients at a low cost due to its low battery consumption and wide network coverage,” said Harris Shim, Head of Business Operations at Lineable.

“SK Telecom has created the first nationwide LoRaWAN™ network and Lineable is one of the first companies to develop a solution that leverages Semtech’s LoRa Technology to track people’s location.”

“Lineable’s Silver wearable technology has already seen early success in Korea by being able to locate Alzheimer patients in real-time,” said Vivek Mohan, Director of Wireless and Sensing Products Group at Semtech. “The LoRa-based device is able help the community and its police force by providing a technology that gives families peace of mind.”

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IoT Business News

Real-time medical imaging AI platform Lunit Insight to aid radiologists

Real-time medical imaging AI platform Lunit INSIGHT

Top international medical AI company Lunit has unveiled a new medical imaging AI platform that enables nodule detection and consolidation in seconds, with near-perfect accuracy.

Chest x-rays and mammography are among the most used diagnostic imaging tests conducted and are vital to the detection of lung and breast cancer. However, the accuracy of traditional methods often falls short of modern expectations. Over 25 percent of lung cancers and 15 percent of breast cancer cases are missed by chest x-ray.

Even the most experienced radiographers stand to benefit from the machine learning capabilities of AI image analysis software. These AIs are given access to a wealth of training data, spotting trends and minute abnormalities that the human eye is simply unable to detect. This sort of complex visual pattern recognition is the perfect candidate for AI assistance, thanks to advancements in deep learning.

Founded in 2013, in South Korea, Lunit develops advanced medical image analytics and novel imaging biomarkers. Their INSIGHT platform claims to be the first ever real-time imaging AI analytics on the web.

Read more: Mako Robot-assisted joint replacement transforms orthopedics

How Lunit’s medical imaging AI platform works

Lunit INSIGHT has been trained with a huge collection of anonymous clinical data from 18 partner hospitals, totalling over 1 million case images.

Using this data, the AI algorithms have learned to detect target diseases and significant radiologic findings, including lung cancer, tuberculosis, pneumonia, pneumothorax, and breast cancer for chest x-ray and mammograms.

“Lunit’s vision is to develop advanced software for medical data analysis and interpretation that goes beyond the level of human vision,” said Anthony Paek, CEO of Lunit. “In presenting Lunit Insight, we hope to contribute in opening a new era of medical practice, by helping and empowering healthcare professionals to make more accurate, consistent, and efficient clinical decisions for the patients.”

Read more: New wearables options for UnitedHealthcare customers

The benefits of AI in medical imaging

When even the latest image recognition AIs can mistake tortoises for guns, the thought of relying on AI to diagnose life-threatening diseases can sound unnerving. Yet, Lunit’s new cloud based AI medical imaging software boasts 97 percent standalone accuracy in nodule detection and 99 percent for consolidation and pneumothorax (the presence of air or gas in the cavity between the lungs and the chest wall).

The Lunit Insight platform is publicly accessible. Anyone can log in and upload x-ray images, which the AI will assess in seconds and return its findings, including the level of abnormality and a visualisation of the AI’s attention map.

Additionally, Lunit solutions can be easily integrated in SaaS API and library incensing format to merge the technology with existing workflows. Nuance, EnvoyAI and Infinitt Healthcare are among the companies set to integrate the software.

Read more: Abilify IoT-enabled digital pills approved amid privacy concerns

Future uses of AI in medical imaging

The chest radiography tool is already available, but the mammography component is timetabled for release early next year. It will detect suspicious breast cancer lesions. Lunit is also looking to develop other medical imaging AI solutions for digital breast tomosynthesis, chest CT, and coronary CT angiography.

Lunit’s AI solutions have been proven to increase the diagnostic performance of its users by up to 20 percent, with non-radiology physicians benefitting from the software significantly. The technology isn’t designed to replace the diagnosis of medical professionals, merely to augment their conclusions – particularly in less definitive cases.

The medical imaging AI company hopes to obtain clinical validation in the coming months. “Large-scale multi-center reader studies are set to be conducted in early 2018,” said Suh, Chief Medical Officer of Lunit. “These are the studies with multiple leading hospitals in Korea and the US for Lunit’s chest x-ray and mammography solutions; publication of the results is targeted for late 2018.”

Likewise, Food and Drug Administration (FDA) approval for Lunit’s chest x-ray and mammography solutions are expected around the same period.

The platform has been ranked in the ‘Top 100 AI Start-ups’ by CB Insights and listed in the ‘Top 5 AI start-ups for Social Impact’ by Nvidia, so it’s gaining attention in the healthcare world and the wider technology space, and justifiably so. The solution’s capability and ease of use has been demonstrated and, with over 1 billion chest x-rays performed worldwide each year, its potential to impact patients’ lives is immense.

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Internet of Business

Real-time disease monitoring unearths power of IoT in agriculture

Real-time disease monitoring unearths power of IoT in agriculture

A new IoT solution, enabling real-time disease monitoring of crops, sees air sampling equipment combined with cellular connectivity and cloud services to boost yields.

Crop infections from pathogens such as bacteria, viruses and fungi have been an issue for farmers ever since we started leaving our hunter-gatherer origins behind and putting down roots. They damage plants, impact quality and limit yield – sometimes devastating whole crops, when they go unchecked.

In the face of an ever-growing global population, agriculture must tackle the daunting task of feeding ourselves sustainably. Current projections indicate that food production must increase by 70 percent by 2050 to meet these needs. The IoT stands to play a huge part in achieving this goal.

Currently, over one billion people face malnutrition due to poor food supply and around twice that lack the nutrients and vitamins required to meet their daily needs. The causes behind the decline of productive agricultural land are multifarious – but the damage caused by pathogens plays a key role, causing 20 to 40 percent of crop losses. Wheat, the source of our bread and pasta, regularly falls prey, suffering around 50 percent losses as a result.

How IoT can boost crop yields

Undoubtedly then, disease monitoring and prevention is central to enhancing yields, Current lab-based techniques, such as polymerase chain reaction [PCR], immunofluorescence [IF] and fluorescence in-situ hybridization [FISH], are expensive and time-consuming.

A new collaboration between scientific equipment producers Burklard, Eseye’s global cellular connectivity and Amazon Web Services [AWS] IoT has created a framework for combatting the problem. Their monitoring solution provides constant air-quality analytics and an early warning system that allows farmers to take targeted action.

Burklard have been in the business of designing and building air samplers for agricultural research since 1953. Their latest product, the Automatic Multi-Vial Cyclone Sampler was developed as part of the UK Government’s Innovate UK project. It offers real-time rapid detection of airborne spore and particle matter, while low energy consumption allows it to be solar powered.

Read more: Italian start-up Evja launches smart agriculture platform for salad growers

From the field to the cloud

The Auto Samplers reside in the farmer’s fields, remotely collecting and analysing spores with what’s known as a LAMP assay – a low-cost, single tube technique for the amplification of DNA. This data is communicated back to the farmer using Eseye’s AnyNet Secure global cellular connectivity and stored within AWS cloud services.

The Eseye Hera 604 (with add-on logger functionality) stores the data and publishes it to AWS. This cloud-based service reduces the need for expensive in-house infrastructure. AWS IoT Gateway tools also do the mathematics behind the forecasting and provide the means to quickly analyse data at scale – allowing farmers to easily see which fields are at risk and treat their crops.

The setup provides farmers with instant access to tailored information from their own fields and complete control over that data. Until now, other solutions may be positioned over 100 miles away from their crops, making farmers reliant on disease forecasts and general predictions. Given the localised nature of pathogens, there is no guarantee the data applies to their own fields.

This means that farmers could be unnecessarily spraying their crops, or neglecting to when needed. Ensuring that herbicides are used as required reduces waste, increases productivity and benefits the environment.

Read more: Dell takes a fresh look at IoT with Aerofarms

What this means for agriculture

“We are finally giving farmers an answer to their concerns over the ramifications of crop disease,” explains Stuart Wili, managing director at Burklard. “This not only provides peace of mind, but the solution also supports the environment and saves precious time, resources and ultimately money.”

“Looking to the future, we plan to roll out the technology across the globe, particularly in developing countries, where the importance of farming is far higher, and therefore the need to prevent disease to ensure a healthy crop is even greater.”

Historically, Burklard used a general modem and SIM card to send alert texts to farmers. The unreliability of rural connectivity presented signal issues, meaning they were constantly changing providers. With AnyNet technology, users can connect to up to 440 cellular operators across 190 countries.

“With the AnyNet Secure SIM, farmers don’t need to rely on single local network coverage, which often can’t be guaranteed,” says Stuart Wili. “Instead they can be assured accurate data from the field is being securely and accurately transmitted back to the server, without any concern over connectivity, the AnyNet Secure SIM will utilise any and all connectivity available.”

Read more: Agrifac and Bilberry team up to beat weeds on Aussie farms

Future of real-time disease monitoring

Paul Marshall, chief customer officer was eager to emphasize the impact the collaboration could have on disease monitoring: “Eseye’s work with Burkard and AWS is a prime example of the range of economic, social and environmental benefits which can be reaped through IoT.”

The internet of things is transforming agriculture, helping to create near-perfect growing conditions, monitor machinery and oversee animal welfare. There are myriad opportunities to record data and empower farmers to make informed decisions.

Beyond the arable landscape, the impact of real-time disease monitoring could have a tremendous impact on healthcare. Newly developed DNA/RNA-based affinity biosensors, not unlike that deployed by Burklard, use nucleic acid fragments for pathogen detection.

The identification of specific DNA sequences could play an important role in the future of clinical human disease monitoring and environmental preservation, alerting us to the presence of disease before symptoms appear – prevention rather than cure.

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Internet of Business

How real-time IIoT actionable data impacts process improvement of warehouses and manufacturers

All those sensors on AGVs (Automated Guided Vehicles) which prevent collisions (with people, equipment, or building structures) are collecting data. Until recently, no AGV manufacturer has found a way to concretise these data and make the actionable, predictive, and fundamentally useful. This is a frequent complaint of Industry 4.0, Big Data, and the Industrial Internet […]

The post How real-time IIoT actionable data impacts process improvement of warehouses and manufacturers appeared first on IoT Now – How to run an IoT enabled business.

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Track asset performance using real-time health scores

As we learned in last week’s post from Kansas City BPU, providing clean, safe water to a large community is a cumbersome task. This post will dive into how one regional water supplier tracks asset performance using real-time health scores. Using data from IoT sensors, they can produce a sustainable, reliable water supply for their community, and improve the lifespan of their most critical assets.

A complex system makes performance monitoring difficult

This supplier owns 174 wells across fifteen well-fields, eleven treatment facilities, eight pumping stations, 240 miles of large diameter pipe, and a large reservoir storage. The reservoir storage is critical during the driest months to maintain supply to customers with limited or no interruption. It is a complex system to manage and optimize. It is important to have a solid grasp of the assets that power their wells, facilities, and stations, as well as the performance and health of these assets.

water reservoir

The storage reservoir used to hold the water supply required during the dry months. Source:

Today’s assessments are condition-based

Like many organizations, asset assessments are done based on condition. Every 3-8 years, the assets are visited and a formal assessment is performed. Using an in-house designed program, one questionnaire per asset type is produced, and this, in combination with photos, is how the status is documented and performance monitored. These assessments are stored in a database and fed into the system that determines when, and if, an asset will be renewed or replaced.

For a safety pump, for example, the types of questions on the questionnaire may include (among others):

  • Are all safety guards present?
  • Is there excessive noise?
  • Is there excessive vibration?
  • Are there any leaks?
  • Is the pump missing any components?
  • Is there unusual smell or heat?
  • Does it meet capacity needs?
  • Is there any corrosion?

Using the data collected, the system can utilize condition information, along with baseline expected asset life and failure curves, to project capital costs that will be needed in the future.

capital budget planning

Projected cost of assets over a 5-year period can help determine capital budget planning.

Moving to Maximo & real-time health scores

The current model is fairly effective but it is neither agile nor predictive. Inspections are years apart, pumps or pipes can go down because maintenance is not performed quickly enough to avoid failure, and recommendations for capital investments are not based on real-time data. The information collected could be months old when it is used to make decisions.

Using real-time condition monitoring by placing IoT sensors on all assets, this supplier monitors data points such as vibration, temperature, battery level and run-time. This data is then input into Maximo. Maximo is the world’s leading enterprise asset management solution, powering nearly every asset-intensive industry in the world. By supplementing their current condition assessments with this sensor data, they perform mini-assessments on a month-to-month basis, rather than every 3-8 years. They can also calculate the remaining useful life of the asset using Maximo. This shift provides them the ability to make better decisions about weekly workload prioritization and capital expenditures.  It also helps in reducing asset failures.

mahi performance dashboard

MAHI dashboard highlighting the asset health map. Source:

From prototype to Maximo Asset Health Insights (MAHI)

Using the IoT for preventive maintenance, you can improve asset maintenance and reduce the potential for failures. MAHI, IBM’s asset health scoring tool, does that for this water supplier. They piloted 45 assets in MAHI, including a mixture of pumps, generators, and motors across multiple sites. They standardized the condition assessment questionnaires, collected meter readings more frequently, incorporated mini-assessments into worker job plans, and determined where data feeds could replace subjective questions. After completing a successful pilot program, this supplier is now eager to expand the program to include an additional 2400 assets.

Learn more about preventive maintenance and MAHI

Read this Aberdeen report on why best-in-class firms are maintaining their most critical assets with EAM & IoT.

Take the first steps towards understanding IoT for Preventative Maintenance.

Learn more about Maximo by experiencing an interactive demo.

References and images for this use case and Kansas City BPU used by permission: MaximoWorld by

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