In parts I and II of this article series, we discussed IoT market trends, explored smart home door lock controllers, and looked at wearables applications with example implementations. In part III, we will illustrate how a wearable implementation can be leveraged to interact with a smart home network.

Making the smart home smarter with wearables

To understand some of the ways wearables can interact with the smart home, let us examine some use cases:

• Smart Kitchen: Smart appliances can prevent accidents by enabling child-hazardous appliances (i.e., oven, stove, dishwasher) to operate only when a parent is nearby (i.e., the parent’s fitness monitor or phone is within range).

• Smart Garage: An automatic garage door can do more than open. An 'I'm Home' feature could be triggered when a person approaches the driveway or door, setting up a pre-programmed sequence of functions such as turning on the lights in the driveway, garage, hallway, and kitchen. The garage door could also adjust home security settings, unlock the interior garage entry door, adjust heating in the house to a preset temperature, and turn on the home audio system to start playing music.

Gesture-based Interactive Control: A wearable device that includes 9-axis motion sensing can determine the orientation of a user’s wrist. This orientation data can be further processed to detect motion-based gestures. Such gestures can be used to control nodes. For instance, when a person is near the front door, he or she can hold their wrist flat, point at the door, and rotate their wrist clockwise to unlock the door or counter-clockwise to lock it (see Figure 3).

Similarly, the gestures shown in Figure 4 can be used to control light intensity when a person is near a room control node. For example, rotating the wrist clockwise increases light intensity while a counter-clockwise motion decreases it (see Figure 5). 

With the presence of multiple static BLE nodes and processing resources available in the wearable device, the location of users with wearables in a household can be triangulated within useful accuracy. This position, along with yaw data (direction) from the 9-axis sensor fusion algorithm, can enable users to point at an object and interact with it. Figure 6 shows an example of a user interacting with a light bulb in a multi-node smart home environment by pointing at it. The door-lock, bulb, and temperature nodes, each with a static location, are used to triangulate the wearable’s location in the house. The yaw data indicates the direction in which the user is pointing. Wrist gestures can be used to send control commands or otherwise interact with the node being pointed at.

PSoC 6 BLE with its state-of-the-art security features, ultra-low power specifications, dual-core architecture and a Bluetooth low energy 4.2 radio fits the bill for both a wearable as well as a smart home device.

To be able to implement capabilities like this where a smart home and wearable device can interact, wearable devices need an embedded MCU with a flexible and integrated architecture. To be able to interface with the many sensors in a wearable device, the embedded MCU must support multiple and different standard digital and analog interfaces. The MCU may also need multiple ADCs for signal acquisition and integrated op amps to reduce component and wearable form factor. If the wearable supports a capacitive touch display, it will need sensors and processing resources to implement the various touch components, including buttons, sliders, and proximity sensors. Figure 7 shows the many features a wearable device may require.

Also, as described previously, a dual-core architecture will enable developers to provide sufficient processing capabilities while maintaining overall low power operation. Figure 8 shows an example of how functions can be partitioned between a low power core (Cortex-M0+) and a high-performance core (Cortex-M4).

Low Power Operation and Efficiency

Low power is essential for wearables, which are often battery powered. An embedded MCU needs to support multiple operating modes to enable developers to optimize power efficiency. In addition to extremely low sleep and hibernation modes, the MCU should be able to dynamically scale the core voltage and frequency.

To understand how dynamic voltage and frequency scaling can save significant power, consider the example of a fingerprint sensor. When the fingerprint sensor is not in use, the system can be clocked down to a lower frequency and voltage, such as 48 MHz and 0.9 V (ULP) core operation. When the fingerprint is enabled and running, indicated by a ‘wake-on finger touch’ interrupt from the fingerprint sensor, the system is clocked up to a higher frequency and voltage to provide real-time processing, on the order of 96 MHz and 1.1 V core operation. Since fingerprint operation is not that frequent, the device runs in the ULP mode most of the time, thus reducing the overall power consumption significantly (see Figure 9).

Another example of optimizing power efficiency is through management of the PMIC controller. The PSoC 6 embedded MCU, for example, can turn OFF the PMIC supplying VDD to itself. The PMIC can be enabled by a switch press. This eliminates the need for dedicated glue logic controlling the PMIC externally. In this power-down mode, the MCU consumes almost no current and can still keep the time. It can also be woken when it is needed.

Security

Secure communications between wearable devices and the smart home network is important. Security can be implemented on many levels within the embedded MCU:

1. BLE Security: The embedded MCU needs to support the latest security updates to the BLE specification, which includes LE secure connections and Link Layer privacy. Link Layer privacy provides a resolvable private address that makes it difficult for hackers to sniff devices.
2. Secure Boot with Over-the-Air (OTA): Secure boot ensures that the MCU executes only trust code. Coupled with Over-the-Air enables the wireless application update and lets only trusted code to execute in the device.
3. Cryptographic Processor: Processing cryptographic methods in hardware significantly simplifies and accelerates complex security algorithms, easing development and minimizing CPU intervention.
4. On-the-fly decryption from external memory: In addition to a cryptographic processor, the embedded can provide on-the-fly encryption/decryption on the serial memory interface. This capability greatly simplifies storage of encrypted code and data in external memory and provides a robust implementation.

Table 1 summarizes many of the functions that can be implemented in smart home appliances and wearable devices.

Table 1. Functions Required for Smart Home Appliances and Wearable Devices

This series has covered many of the important aspects of smart home appliance/controller and wearable device design. With a dual-core architecture, developers can provide the processing capabilities required for real-time analysis of sensor data while managing real-time system tasks with the greatest power efficiency.

References:

1. PSoC® 6 MCU: PSoC 63 with BLE Datasheet Programmable System-on-Chip (PSoC®)
2. PSoC 63 with BLE Architecture Technical Reference Manual (TRM)
3. AN210781 - Getting Started with PSoC 6 MCU with Bluetooth Low Energy (BLE) Connectivity
4. AN219528 - PSoC(R) 6 MCU Low-Power Modes and Power Reduction Techniques
5. AN215656 – PSoC 6 MCU Dual-Core CPU System Design
6. AN213924 - PSoC 6 MCU Bootloader Software Development Kit (SDK) Guide
7. AN217666 – PSoC 6 MCU Interrupts
8. AN218241 - PSoC 6 MCU Hardware Design Considerations
9. AN92239 - Proximity Sensing with CapSense®
10. AN219434 - Importing PSoC Creator Code into an IDE for a PSoC 6 Project
11. AN91445 - Antenna Design and RF Layout Guidelines

Authors:

Jaya Kathuria works as an Applications Manager at Cypress Semiconductor Corporation where she is managing the Embedded Applications Group and Solutions Development using the PSoC platform. She has 13+ years of experience in the Semiconductor Industry. She earned an executive management credential from IIM, Bangalore and holds a BS in Electronics Engineering from the Kurukshetra University. Jaya can be reached at [email protected]. 

Meenakshi Sundaram is a Senior Staff Applications Engineer at Cypress Semiconductor Corporation. He works on Bluetooth low energy and System-on-Chip solution development with Cypress’ PSoC platform. He has 7+ years’ experience in Embedded System and Solution development. He holds a MS degree in Embedded Systems from Birla Institute of Technology, Pilani. He can be reached at [email protected].