March 27 2018
LTE Cat M1 Modules Provide an Alternative to Broadband and Smartphones for Assisted Living Systems
According to projections made by the United Nations, the number of people aged 60 or over will account for 22% of the world’s population by 2050, a figure that was just 10% in the year 2000. Over the same period of time, the total number of people alive on the planet will reach 8 billion. In absolute terms there will be over 1.7 billion people in the age group of 60+. While we are living longer and remaining active into our later years, there can be no doubt that an increase in the number of elderly in our society will put greater pressure on the healthcare systems of the world.
This is why many believe that the nascent Internet of Medical Things, or IoMT, will rapidly mature to become a significant part of the solution ranging from wearable technology that monitors conditions such as diabetes, to providing a near constant status of cardiac activity. Technology in the form of personal assistants will also remind patients to take their medications in the event that they forget. There are already several high profile examples of these, but we can expect to see more bespoke alternatives specifically for healthcare. Companies specializing in artificial intelligence platforms that use natural speech recognition are already attracting customers. The use of robotics is also likely to increase in this area as well.
The early diagnosis of new, emerging or potential conditions is another area where technology can supplement the human touch, such as using in-home systems to take blood pressure, temperature and pulse. Connected devices are now available that include a range of devices such as ECG monitors, glucometers, and spirometers. The data is uploaded to a cloud platform where it can be accessed by the patient or a medical professional.
The emergence of cloud platforms for connected medical devices will help accelerate this new paradigm, with commercial solutions competing with open source projects like Kaa. These solutions will be classified as a software as a service (SaaS) or platform as a service (PaaS), and may join with providers of electronic health record (EHR) systems that are already well established in the market.
As well as using technology to monitor medical conditions, it will also be used to help the elderly be independent longer through assisted living. This incudes simple but effective ways of ensuring someone living alone is still able to care for themselves by monitoring their general activity, or their use of electrical appliances.
It is quite possible that this is also the group of people least likely to have a broadband connection or smartphone to provide the backhaul connection for connected assisted living solutions. Without a reliable backhaul there would be no way to obtain monitoring data. Those without broadband or a smartphone could fall into a forgotten minority.
Fortunately, there is a solution in the form of cellular connectivity. Release 13 of the 3GPP specification ushered in several new technologies for the IoT, collectively referred to as LTE-M. They are more generally categorized as LPWAN, or low power wide area network technologies, which address the need for a low bandwidth connection over long distances without the complexity of building and managing an entire network infrastructure. Building these technologies into the LTE specification opens up new opportunities in the IoT.
Image of LTE Cat M1 across all sectors
Figure 1: LTE Cat M1 has the potential to help shape the IoT across all sectors, including healthcare. (Source: u-blox)
The technologies introduced include enhancements to eMTC, also known as LTE Cat M1, which supports low to medium data rates and over-the-air firmware updates. It also includes modes that improve indoor coverage, as well as support for both cell tower handover (critical for any mobile device) and voice over LTE (VoLTE), which could also be seen as crucial for any platform intended to provide healthcare. Despite all these features, LTE Cat M1 modules can still achieve very long operation times from a single primary cell battery, in the region of 10 years in some cases.
These modules could easily be used as the heart of a connected home designed to provide assisted living features, and equally be used out of the home for continuity of service without becoming a cumbersome burden for the user. Several manufacturers are now offering LTE Cat M1 enabled modules for this very purpose.
The SARA-R4 from u-blox supports both LTE Cat M1 and LTE Cat NB1; the latter offers lower bandwidth and is oriented more towards simpler IoT endpoints such as smart sensors, but could also be used for assisted living applications.
Image of SARA-R4 from u-blox
Figure 2: The SARA-R4 from u-blox measures just 16 mm by 25 mm by 2.5 mm and provides both LTE Cat M1 and LTE NB-IOT connectivity. (Source: u-blox)
While the module accepts and understands AT commands, and includes I2C, SPI, UART and USB interfaces, the device can present itself at a system level as a USB device, meaning it can be connected to and controlled by any host with the relevant drivers. As the interface is USB 2.0-compliant with a maximum transfer rate of 480 kbit/s, the USB interface would be the most efficient way of transferring data to and from a host processor.
Figure 3 shows the Skywire LTE Cat M1 module (part number NL-SW-LTE-SVZM20) from NimbeLink. This module is supplied with FCC certification for use in an end-device, which means it can be put into service without any further testing or qualification procedures, making this module ideal for a connected platform. The module’s interface is compatible with the XBee interface standard, making it able to be used with a wide range of development kits or host boards.
Image of Skywire LTE Cat M1 module from NimbeLink
Figure 3: The Skywire LTE Cat M1 module from NimbeLink is designed to be embedded into products ready for volume production. (Source: NimbeLink)
To support developers, NimbeLink has also created the NL-M1DK development kit (Figure 4). The block diagram of the kit shown in Figure 5 denotes its Arduino-compatible interface, effectively making it a shield. It can, however, also be connected over USB to a PC. The kit comes with an antenna and an LTE SIM card. Once configured and enumerated as a USB device by the PC, it can be controlled using AT commands sent from any suitable terminal program running on a Windows® machine.
Image of NL-M1DK development kit from NimbeLink
Figure 4: The NL-M1DK development kit from NimbeLink helps developers get up and running with LTE Cat M1 connectivity. (Source: NimbeLink)
Diagram of NL-M1DK development kit offers Arduino-compatible headers
Figure 5: The NL-M1DK development kit offers Arduino-compatible headers. (Source: NimbeLink)
The small outline of these LTE Cat M1 modules means they can easily be integrated into products intended to be carried by a patient or elderly person. Through the integration of additional sensors, such a device could become a way of detecting if someone has suffered a fall, or conversely, remained inactive for an extended period. Using cloud-based AI, the patterns of an individual can quickly be learned, enabling the platform to alert a care provider if anything abnormal occurs.
For example, the BNO080 from Hillcrest Laboratories is a 9-axis inertial measurement unit that integrates three sensors alongside an Arm® Cortex®-M0+ microcontroller. The three sensors comprise a triaxial 12-bit accelerometer, a triaxial 16-bit gyroscope and a triaxial geomagnetic sensor developed by Bosch Sensortec, while the microcontroller hosts Hillcrest’s SH-2 software. This includes its MotionEngine DSP software that interprets the raw data provided by the MEMS sensors and turns it into a precise representation of the device’s motion.
By adding PAN (personal area network) connectivity to the device through Bluetooth, ZigBee, or Thread, it can also become a home hub able to communicate with household appliances or other smart sensors designed to provide assisted living. This could include sensors on windows and doors, for example, or temperature sensors in each room. Light sensors would provide further information such as whether or not the curtains have been opened that day.
Examples of possible sensors include the BH1900NUX-TR from Rohm Semiconductor, which uses a two-wire interface to provide a digital representation of a temperature within the range of -20°C to +85°C with an accuracy of ±3.0°C. The DRV5032DUDMRT ultra-low-power digital switch Hall effect sensor from Texas Instruments could be used in the design of a window/door monitoring switch; when configured to update its output at 5 Hz it consumes just 0.54 μA at a supply voltage of 1.8 V. The TMD27723WA digital ambient light sensor from ams has a response that closely approximates the human eye, making it ideal to monitor the light levels in a room.
Uploaded to a cloud-based platform and analyzed using AI, all of this ‘small data’ could contribute to a ‘big data’ view of the user to provide monitoring with a minimal amount of privacy invasion.
Using a Raspberry Pi 3 as a host, coupled with the NL-AB-RPI Hat or the NL-M1DK development kit and a Skywire LTE Cat M1 modem, it is relatively simple to start developing connected devices. After installing Raspbian and the PPP (point to point protocol) package on the Pi, executing AT commands through a suitable terminal program will bring the module online.
Building a smart hub for assisted living can be quick and easy, too. There are a growing number of cloud service providers offering platforms tailored to the IoT; one example is Amazon’s AWS IoT services. Once connected, the hub can be used to push data to the cloud for further processing.
Amazon’s services include: the AWS IoT core, which is a managed cloud platform that supports secure interactions between devices and the cloud; the AWS IoT device management service, which provides the scale necessary to manage large numbers of devices; and AWS IoT Analytics, which is a managed service for the analysis of data with support for machine learning, allowing data to become insight.