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README.md
168
README.md
@ -1,8 +1,5 @@
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# SuperSensor v2.x
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**NOTICE**: The Supersensor v2.x is still under development! Parts and configurations
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may change until the design is finalized.
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The SuperSensor is an all-in-one voice, motion, presence, temperature/humidity/air
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quality, and light sensor, built on an ESP32 with ESPHome, and inspired
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heavily by the EverythingSmartHome Everything Presence One sensor and the
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@ -21,7 +18,8 @@ it bare if you like the "PCB on a wall" aesthetic.
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To Use:
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* Install the ESPHome configuration `supersensor.yaml` to a compatible ESP32 devkit (below).
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* Install the ESP32 and sensors into the custom PCB.
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* Install the ESP32 and sensors into the custom PCB (if desired).
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* [Optional] 3D Print the custom case.
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* Power up the SuperSensor, connect to the WiFi AP, and connect it to your network.
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* Install the SuperSensor somewhere that makes sense.
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* Add/adopt the SuperSensor to HomeAssistant using the automatic name.
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@ -41,15 +39,15 @@ and [my update post on version 2.0](https://www.boniface.me/the-supersensor-2.0)
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## Major Changes from 1.x
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1. Replaced the Bosch BME680 with the Sensirion SHT45 and Sensirion SGP41.
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1. Replaced the Bosch BME680 with the Sensirion SHT45 and Sensirion SGP30.
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The BME680 proved to be woefully unreliable in my testing. Temperature was fairly accurate (internal heating and offset notwithstanding),
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but humidity was wildly off of what other thermometers/hydrometers would report. In addition, the AQ functionality of the sensor was a
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source of much frustration and I was never able to get it to work reliably, either with the official BSEC library or with my own attempts
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at self-configuration.
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Thus, this sensor has been replaced with two Sensirion sensors which in my experience so far have been much more reliable and consistent.
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There is a slight cost increase due to these sensors, but not signfigant enough to outweigh the benefit of reliable monitoring they confer.
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Thus, this sensor has been replaced with two Sensirion sensors which in my experience so far have been much more reliable and consistent,
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and the cost difference is negligible.
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2. Replaced the SR602 PIR sensor with the AM312 PIR sensor.
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@ -63,12 +61,15 @@ and [my update post on version 2.0](https://www.boniface.me/the-supersensor-2.0)
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3. Completely redesigned the custom PCB around the above sensor changes, which is now more compact in a 50x55mm almost-square configuration.
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4. Significantly cleaned up the ESPHome configuration, to support the above sensors and remove a lot of cruft that was caused by the BME680.
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This includes a new set of custom AQ calculations based on the SGP30 and SHT45 sensors that, while not necessarily following the full EPA
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IAQI spec, should still give a reasonable view of the air quality conditions of an interior room and not deviate wildly and nonsensically
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like the BME680 did. Details of the calculation are provided below.
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## Parts List
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| Qty | Component | Cost (2025/05 CAD, ex. shipping) | Links |
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|-------|--------------------|----------------------------------|-------|
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| 1 | GY-SGP41 | $11.08 | [AliExpress](https://www.aliexpress.com/item/1005006746827606.html) |
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| 1 | GY-SGP30 | $5.73 | [AliExpress](https://www.aliexpress.com/item/1005008473372972.html) |
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| 1 | GY-SHT45 | $5.67 | [AliExpress](https://www.aliexpress.com/item/1005008175340220.html)* |
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| 1 | SR602 | $0.81 | [AliExpress](https://www.aliexpress.com/item/1005001572550300.html) |
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| 1 | TSL2591 | $4.59 | [AliExpress](https://www.aliexpress.com/item/1005008619462097.html) |
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@ -77,59 +78,16 @@ and [my update post on version 2.0](https://www.boniface.me/the-supersensor-2.0)
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| 1 | ESP32 HW-395 | $6.67 | [AliExpress](https://www.aliexpress.com/item/1005006019875837.html)* |
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| 2 | RBG LED | $0.09 ($9.12/100) | [Amazon](https://www.amazon.ca/dp/B09Y8M2PKS) |
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| 1 | 470Ω resistor | $0.08 ($7.99/100) | [Amazon](https://www.amazon.ca/dp/B08MKQX2XT) |
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| 2 | Female pin header† | $1.59 ($15.99/10) | [Amazon](https://www.amazon.ca/dp/B08CMNRXJ1) |
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| 1 | Female pin header† | $1.59 ($15.99/10) | [Amazon](https://www.amazon.ca/dp/B08CMNRXJ1) |
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| 1 | Custom PCB (JLC) | $0.69 ($6.89/10) | [GitHub](https://github.com/joshuaboniface/supersensor) |
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| **TOTAL** | | **$40.58** | |
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| 1 | 3D Printed case | $?.??‡ | [GitHub](https://github.com/joshuaboniface/supersensor) |
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| **TOTAL** | | **$33.64** | |
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`*` Ensure you select the correct device on the page as it shows multiple options.
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`†` One of these sets is optional, and is useful if you do not want to solder the individual sensors directly to the board (see below).
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`†` This is optional and only required if you don't want to directly solder the ESP32 to the board, but I recommend it.
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### To Solder or Not To Solder
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Personally, for my Supersensor 1.x's and the initial batch of Supersensor 2.x's, I directly soldered
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all the non-ESP components to the board. This proved to be a major mistake when I later decided
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to switch from SGP30's to SGP41's after some testing and I had to desolder all of them, ruining
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several PCBs in the process. It was also a hassle to desolder the existing sensors for reuse
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during the 1.x to 2.x conversion.
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As a result, I actually strongly encourage anyone building one of these units to leverage sockets
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for all components, to allow for quick swapping if any turn out to be defective or if future changes
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are warranted.
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Note that due to the PCB design, you *must* socket at least one set of components - either the ESP32
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or the sensors on the front. Due to the positioning and overlap, it would be impossible to solder
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everything directly to the board, as the ESP covers several of the solder points of the front
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sensors and vice versa.
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You can use the provided 40-pin female headers exclusively if you wish, and cut them to length for
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the individual sensors as needed, or you can use individually-sized female headers in the following
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quantities should you wish for a slightly neater finish:
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* 3x 3-pin (AM302, INMP441 x2)
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* 2x 4-pin (SGP41, SHT45)
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* 1x 5-pin (LD2410C)
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* 1x 6-pin (TSL2591)
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I will leave it up to the reader to source these specific sizes if they desire (I found all except
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a 5-pin on Amazon, and just used a 6-pin with one pin removed).
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I still directly solder the RGB LEDs and resistor to the board for simplicity as these very small
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leads are not easily socketed, and these components are so inexpensive as to be effectively
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disposable along with the PCB should that be required.
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### Part Swaps
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To save a little money, it is possible to swap out the two Sensirion sensors for their less-feature-
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rich peers, with no code changes:
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* SGP41 -> SGP40 - removes the NOx functionality
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* SHT45 -> SHT40/41/43 - less accuracy
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Personally, I do not find the minimal cost savings to be worth sacrificing the extra potential
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functionality, so I recommend using the provided models, but this is up to the builder to decide.
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No other parts can be easily swapped without code or PCB design changes.
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`‡` Providing a price is impossible due to the wide range of possible fillament types and brands, but should be negligible.
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## Configurable Options
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@ -151,19 +109,17 @@ SuperSensors in a single room and only want one to respond to voice commands.
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If enabled (the default), when overall presence is detected, the LEDs will
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glow "white" at 15% power to signal presence.
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### Temperature Offset (selector, -30 to +10 @ 0.1, -5 default)
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### Temperature Offset (selector, -10 to +5 @ 0.1, -5 default)
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Allows calibration of the SHT45 temperature sensor with an offset from -30 to +10
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Allows calibration of the SHT45 temperature sensor with an offset from -10 to +5
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degrees C. Useful if the sensor is misreporting actual ambient tempreatures. Due
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to internal heating of the SHT45 by the ESP32, this defaults to -5; further
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calibration may be needed for your sensors and environment based on an external
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reference.
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calibration may be needed for your sensors and environment.
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### Humidity Offset (selector, -20 to +20 @ 0.1)
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### Humidity Offset (selector, -10 to +10 @ 0.1)
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Allows calibration of the SHT45 humidity sensor with an offset from -10 to +10
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percent relative humidity. Useful if the sensor is misreporting actual humidity
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based on an external reference.
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percent relative humidity. Useful if the sensor is misreporting actual humidity.
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### PIR Hold Time (selector, 0 to +60 @ 5, 0 default)
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@ -271,28 +227,70 @@ is likely not useful.
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## AQ Details
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The SuperSensor 2.0 features an SGP41 air quality sensor by Sensirion. This is a powerful AQ
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sensor which powers several commercial devices including the AirGradient One, which gave
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us a lot of our configuration via their sharing of algorithms.
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The SuperSensor 2.x provides 2 base air quality sensors (numeric), from which
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4 human-readable text sensors are derived.
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The sensor provides two base readings: a VOC Index, and a NOx Index. These values are both
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floating references centered at 100 (VOC) and 1 (NOx), where that value represents "normal"
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air over the previous 24 hours. These sensors are very useful for any sort of quick-change
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automations, e.g. turn on a fan if levels spike due to cooking.
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The goal of these sensors is to track general comfort and livability in a
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room, not specific contaminants or conditions. Because the SGP30 can only
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track TVOC and eCO2, we do not track particulates, CO, NOx, or CH2O, all
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of which are required for a full EPA (I)AQI score. This means the best
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we can do is approximate (I)AQI roughly, and since a scale of 0-500 based
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on approximations seems pointless, I went with much simpler 1-4/5 scores
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instead. I feel this does a good enough job to be useful for 99% of rooms.
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In addition, we leverage AirGradient's published forumulas to convert the VOC index into
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actual VOC quantities, in both µg/m³ and ppb. While this may drift due to the sensor's regular
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internal recalibration, I feel that following what AirGradient does is sufficient enough
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for any real-world home usage. Further, we use a very rough conversion of the aforementioned
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VOC quantity into an eCO2 reading, using Isobutylene as a reference gas. These sensors are
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more useful for display purposes, to show the current levels in a room in a dashboard or
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other such place, for human consumption. Note that no such conversions are done for NOx as
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there are no (that I can find) published empirical calculations for this conversion, unlike
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for VOCs via AirGradient.
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We also cannot really debate whether the BME680 is actually any more accurate
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in this regard, since their algorithms are proprietary and all that is exposed
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normally is a single resistance value, so in my opinion this is actually
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superior to that sensor anyways with two discrete datapoints (versus one),
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even if it does still seem limited when compared to dedicated AQ sensors.
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And that is to say nothing of the issues with that sensor (constantly climbing
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IAQ values over time, poor calibration, etc.).
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Note however that like all MOx sensors, the SGP41 does not differentiate gasses, and as
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such cannot tell the difference between normal, everyday natural VOCs like those in
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breath or from e.g. ripening fruit, and dangerous VOCs from e.g. construction materials.
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These should be used only as a general indication of air quality over short periods, rather
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than an absolute reference over long periods (much to my own frustration but inevitable
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begruding acceptance).
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### Base Numeric Values
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#### IAQ Index (1-5)
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The IAQ index is calculated based on the TVOC and eCO2 values from the SGP30
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sensor, to provide 5 levels of air quality. This corresponds approximately
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to the levels provided by the BME680 (0-50, 50-100, 100-200, 200-300, 300+).
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5 is "excellent": the TVOC is <65 ppb and the eCO2 is <600 ppm.
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4 is "good": the TVOC is 65-220 ppb or the eCO2 is 600-800 ppm.
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3 is "moderate": the TVOC is 220-660 ppb or the eCO2 is 800-1200 ppm.
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2 is "poor": the TVOC is 660-2200 ppb or the eCO2 is 1200-2000 ppm.
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1 is "unhealthy": the TVOC is >2200 ppb or the eCO2 is >2000 ppm.
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#### Room Health Score (1-4)
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The Room Health Score is calculated based on the IAQ, temperature, and humidity,
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and is designed to show how "nice" a room is to be in. Generally a 4 is a nice
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place to be, especially for someone with respiratory issues like myself, and lower
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scores indicate more deviations from the norms or poor IAQ.
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4 is "optimal": IAQ is >= 4 ("excellent" or "good"), temperature is between 18C and 24C, and humidity is between 40% and 60%.
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3 is "fair": One of the above is not true, and IAQ is >= 3 ("moderate").
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2 is "poor": Two of the above are not true, and IAQ is >= 2 ("poor").
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1 is "bad": All of the above are not true or IAQ is 1 ("unhealthy") regardless of other values.
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Note that IAQ levels hold a major sway over this level, and decreasing IAQ
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scores will push the room score lower regardless of temperature or humidity.
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It is best used together with the individual sensors to determine exactly
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what is wrong with the room.
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### Derived Text Sensors
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#### VOC Level
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This reports the VOC level alone, based on the scale under IAQ Index, in textual form ("Excellent, "Good", etc.).
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#### CO2 Level
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This reports the eCO2 level alone, based on the scale under IAQ Index, in textual form ("Excellent, Good", etc.).
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#### IAQ Classification
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This reports the IAQ Index in textual form ("Excellent", "Good", etc.).
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#### Room Health
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This reports the Room Health Score in textual form ("Optimal", "Fair", "Poor", "Bad").
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File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
375
supersensor.yaml
375
supersensor.yaml
@ -103,11 +103,6 @@ globals:
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restore_value: no
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initial_value: "0"
|
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|
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- id: current_wake_word
|
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type: std::string
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restore_value: yes
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initial_value: '"mww_computer"'
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|
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script:
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- id: light_off
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then:
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@ -258,17 +253,6 @@ script:
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}
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|
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interval:
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# Regular MWW state check every 30s
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- interval: 30s
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then:
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- if:
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condition:
|
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- switch.is_on: enable_voice_support
|
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- not:
|
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micro_wake_word.is_running
|
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then:
|
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- micro_wake_word.start:
|
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|
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# Regular occupancy state reporting to HASS every 30s
|
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- interval: 30s
|
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then:
|
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@ -289,7 +273,7 @@ interval:
|
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}
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|
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logger:
|
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level: DEBUG
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level: WARN
|
||||
baud_rate: 115200
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|
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api:
|
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@ -346,9 +330,6 @@ time:
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then:
|
||||
- logger.log: "Time synchronized with Home Assistant"
|
||||
|
||||
debug:
|
||||
update_interval: 15s
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||||
|
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uart:
|
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id: ld2410_uart
|
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rx_pin: GPIO19
|
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@ -383,11 +364,9 @@ micro_wake_word:
|
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id: mww
|
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microphone:
|
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microphone: mic
|
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gain_factor: 8
|
||||
gain_factor: 4
|
||||
stop_after_detection: false
|
||||
models:
|
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- model: github://genehand/Custom_V2_MicroWakeWords/models/computer/computer.json@update-json
|
||||
id: mww_computer
|
||||
- model: github://esphome/micro-wake-word-models/models/v2/hey_jarvis.json
|
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id: mww_hey_jarvis
|
||||
- model: github://esphome/micro-wake-word-models/models/v2/hey_mycroft.json
|
||||
@ -413,8 +392,8 @@ voice_assistant:
|
||||
micro_wake_word: mww
|
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use_wake_word: false
|
||||
noise_suppression_level: 3
|
||||
auto_gain: 31 dbfs
|
||||
volume_multiplier: 4
|
||||
auto_gain: 4 dbfs
|
||||
volume_multiplier: 8
|
||||
on_wake_word_detected:
|
||||
- logger.log: "Wake word detected in VA pipeline"
|
||||
- light.turn_on:
|
||||
@ -438,7 +417,7 @@ voice_assistant:
|
||||
brightness: 75%
|
||||
red: 0
|
||||
green: 1
|
||||
blue: 1
|
||||
blue: 1
|
||||
on_tts_start:
|
||||
- if:
|
||||
condition:
|
||||
@ -536,75 +515,34 @@ sensor:
|
||||
return heap_caps_get_free_size(MALLOC_CAP_INTERNAL) / 1024;
|
||||
entity_category: diagnostic
|
||||
|
||||
- platform: debug
|
||||
free:
|
||||
name: "Heap Free"
|
||||
block:
|
||||
name: "Heap Max Block"
|
||||
loop_time:
|
||||
name: "Loop Time"
|
||||
cpu_frequency:
|
||||
name: "CPU Frequency"
|
||||
|
||||
- platform: sgp4x
|
||||
voc:
|
||||
name: "SGP41 VOC Index"
|
||||
id: sgp41_voc_index
|
||||
accuracy_decimals: 0
|
||||
icon: mdi:waves-arrow-up
|
||||
filters:
|
||||
- sliding_window_moving_average: # We take a reading every 15 seconds, but calculate the sliding
|
||||
window_size: 12 # average over 12 readings i.e. 60 seconds/1 minute to normalize
|
||||
send_every: 3 # brief spikes while still sending a value every 15 seconds.
|
||||
nox:
|
||||
name: "SGP41 NOx Index"
|
||||
id: sgp41_nox_index
|
||||
accuracy_decimals: 0
|
||||
icon: mdi:waves-arrow-up
|
||||
- platform: sgp30
|
||||
eco2:
|
||||
name: "SGP30 eCO2"
|
||||
id: sgp30_eco2
|
||||
accuracy_decimals: 1
|
||||
filters:
|
||||
- sliding_window_moving_average:
|
||||
window_size: 12
|
||||
send_every: 3
|
||||
window_size: 20
|
||||
send_every: 1
|
||||
tvoc:
|
||||
name: "SGP30 TVOC"
|
||||
id: sgp30_tvoc
|
||||
accuracy_decimals: 1
|
||||
filters:
|
||||
- sliding_window_moving_average:
|
||||
window_size: 20
|
||||
send_every: 1
|
||||
eco2_baseline:
|
||||
name: "SGP30 Baseline eCO2"
|
||||
id: sgp30_baseline_ec02
|
||||
tvoc_baseline:
|
||||
name: "SGP30 Baseline TVOC"
|
||||
id: sgp30_baseline_tvoc
|
||||
compensation:
|
||||
temperature_source: sht45_temperature
|
||||
humidity_source: sht45_humidity
|
||||
store_baseline: true
|
||||
update_interval: 5s
|
||||
|
||||
- platform: template
|
||||
name: "SGP41 TVOC (µg/m³)"
|
||||
id: sgp41_tvoc_ugm3
|
||||
icon: mdi:molecule
|
||||
lambda: |-
|
||||
float i = id(sgp41_voc_index).state;
|
||||
if (i < 1) return NAN;
|
||||
float tvoc = (log(501.0 - i) - 6.24) * -878.53;
|
||||
return tvoc;
|
||||
unit_of_measurement: "µg/m³"
|
||||
accuracy_decimals: 0
|
||||
|
||||
- platform: template
|
||||
name: "SGP41 TVOC (ppb)"
|
||||
id: sgp41_tvoc_ppb
|
||||
icon: mdi:molecule
|
||||
lambda: |-
|
||||
float tvoc_ugm3 = id(sgp41_tvoc_ugm3).state;
|
||||
float tvoc_ppm = tvoc_ugm3 * 0.436; // ppb estimated using isobutylene MW (56.1 g/mol)
|
||||
return tvoc_ppm;
|
||||
unit_of_measurement: "ppb"
|
||||
accuracy_decimals: 0
|
||||
|
||||
- platform: template
|
||||
name: "SGP41 eCO2 (appr.)"
|
||||
id: sgp41_eco2_appr
|
||||
icon: mdi:molecule-co2
|
||||
lambda: |-
|
||||
float tvoc_ppb = id(sgp41_tvoc_ppb).state;
|
||||
float eco2_ppm = 400.0 + 1.5 * tvoc_ppb;
|
||||
if (eco2_ppm > 2000) eco2_ppm = 2000;
|
||||
return eco2_ppm;
|
||||
unit_of_measurement: "ppm"
|
||||
accuracy_decimals: 0
|
||||
store_baseline: yes
|
||||
update_interval: 15s
|
||||
|
||||
- platform: sht4x
|
||||
temperature:
|
||||
@ -634,9 +572,9 @@ sensor:
|
||||
humidity: sht45_humidity
|
||||
id: sht45_absolute_humidity
|
||||
|
||||
# Dew Point
|
||||
- platform: template
|
||||
name: "SHT45 Dew Point"
|
||||
icon: mdi:thermometer-water
|
||||
id: sht45_dew_point
|
||||
unit_of_measurement: "°C"
|
||||
lambda: |-
|
||||
@ -648,38 +586,47 @@ sensor:
|
||||
return (b * alpha) / (a - alpha);
|
||||
update_interval: 15s
|
||||
|
||||
# IAQ Index (1-5, 5=Great))
|
||||
- platform: template
|
||||
name: "IAQ Index"
|
||||
id: iaq_index
|
||||
lambda: |-
|
||||
int tvoc = id(sgp30_tvoc).state;
|
||||
int eco2 = id(sgp30_eco2).state;
|
||||
if (tvoc > 2200 || eco2 > 2000) return 1; // Bad
|
||||
if (tvoc > 660 || eco2 > 1200) return 2; // Poor
|
||||
if (tvoc > 220 || eco2 > 800) return 3; / Fair
|
||||
if (tvoc > 65 || eco2 > 500) return 4; // Good
|
||||
return 5; // Great
|
||||
update_interval: 15s
|
||||
|
||||
# Room Health Score (1-4, 4=Optimal)
|
||||
- platform: template
|
||||
name: "Room Health Score"
|
||||
id: room_health_score
|
||||
unit_of_measurement: "%"
|
||||
accuracy_decimals: 0
|
||||
icon: mdi:home-heart
|
||||
id: room_health
|
||||
lambda: |-
|
||||
float voc_index = id(sgp41_voc_index).state;
|
||||
float temp = id(sht45_temperature).state;
|
||||
float humidity = id(sht45_humidity).state;
|
||||
|
||||
// VOC Score (0–100)
|
||||
float voc_score = 0;
|
||||
if (voc_index <= 100) voc_score = 100;
|
||||
else if (voc_index <= 200) voc_score = 80;
|
||||
else if (voc_index <= 300) voc_score = 60;
|
||||
else if (voc_index <= 400) voc_score = 40;
|
||||
else if (voc_index <= 500) voc_score = 50;
|
||||
else voc_score = 0;
|
||||
|
||||
// Temperature Score (0–100)
|
||||
float temp_score = 100.0 - abs(temp - 23.0) * 10.0;
|
||||
if (temp_score < 0) temp_score = 0;
|
||||
|
||||
// Humidity Score (0–100), ideal range 35–55%
|
||||
float humidity_score = 100.0 - abs(humidity - 50.0) * 3.0;
|
||||
if (humidity_score < 0) humidity_score = 0;
|
||||
|
||||
// Weighted average
|
||||
float overall_score = (voc_score * 0.5 + temp_score * 0.25 + humidity_score * 0.25);
|
||||
|
||||
return round(overall_score);
|
||||
float rh = id(sht45_humidity).state;
|
||||
int iaq = id(iaq_index).state;
|
||||
|
||||
bool temp_ok = (temp >= 18 && temp <= 24);
|
||||
bool hum_ok = (rh >= 40 && rh <= 60);
|
||||
bool iaq_ok = (iaq >= 4);
|
||||
|
||||
int conditions_met = 0;
|
||||
if (temp_ok) conditions_met++;
|
||||
if (hum_ok) conditions_met++;
|
||||
if (iaq_ok) conditions_met++;
|
||||
|
||||
if (iaq_ok && temp_ok && hum_ok) {
|
||||
return 4; // Optimal: All conditions met and IAQ is excellent/good
|
||||
} else if (iaq >= 3 && conditions_met >= 2) {
|
||||
return 3; // Fair: IAQ is moderate and at least 2 conditions met
|
||||
} else if (iaq >= 2 && conditions_met >= 1) {
|
||||
return 2; // Poor: IAQ is poor and at least 1 condition met
|
||||
} else {
|
||||
return 1; // Bad: All conditions failed or IAQ is unhealthy
|
||||
}
|
||||
update_interval: 15s
|
||||
|
||||
- platform: tsl2591
|
||||
@ -732,9 +679,10 @@ binary_sensor:
|
||||
name: "SuperSensor Occupancy"
|
||||
id: supersensor_occupancy
|
||||
device_class: occupancy
|
||||
on_state:
|
||||
then:
|
||||
- script.execute: light_off
|
||||
on_press:
|
||||
- script.execute: light_off
|
||||
on_release:
|
||||
- script.execute: light_off
|
||||
|
||||
- platform: gpio
|
||||
name: "PIR GPIO"
|
||||
@ -783,10 +731,6 @@ binary_sensor:
|
||||
name: "LD2410C Still Target"
|
||||
|
||||
text_sensor:
|
||||
- platform: version
|
||||
name: "ESPHome Version"
|
||||
entity_category: diagnostic
|
||||
|
||||
- platform: wifi_info
|
||||
ip_address:
|
||||
name: "WiFi IP Address"
|
||||
@ -797,44 +741,57 @@ text_sensor:
|
||||
mac_address:
|
||||
name: "WiFi MAC Address"
|
||||
|
||||
- platform: debug
|
||||
device:
|
||||
name: "Device Info"
|
||||
reset_reason:
|
||||
name: "Reset Reason"
|
||||
|
||||
- platform: ld2410
|
||||
version:
|
||||
name: "LD2410C Firmware Version"
|
||||
mac_address:
|
||||
name: "LD2410C MAC Address"
|
||||
|
||||
# VOC Level
|
||||
- platform: template
|
||||
name: "Chemical Pollution"
|
||||
id: sgp41_chemical_pollution
|
||||
icon: mdi:molecule
|
||||
name: "VOC Level"
|
||||
lambda: |-
|
||||
float voc_index = id(sgp41_voc_index).state;
|
||||
if (voc_index < 1 || voc_index > 500) return {"Unknown"};
|
||||
if (voc_index <= 100) return {"Excellent"};
|
||||
else if (voc_index <= 200) return {"Good"};
|
||||
else if (voc_index <= 300) return {"Moderate"};
|
||||
else if (voc_index <= 400) return {"Unhealthy"};
|
||||
else return {"Hazardous"};
|
||||
int tvoc = id(sgp30_tvoc).state;
|
||||
if (tvoc < 65) return {"Great"};
|
||||
if (tvoc < 220) return {"Good"};
|
||||
if (tvoc < 660) return {"Fair"};
|
||||
if (tvoc < 2200) return {"Poor"};
|
||||
return {"Bad"};
|
||||
update_interval: 15s
|
||||
|
||||
# CO2 Level
|
||||
- platform: template
|
||||
name: "CO2 Level"
|
||||
lambda: |-
|
||||
int eco2 = id(sgp30_eco2).state;
|
||||
if (eco2 < 500) return {"Great"};
|
||||
if (eco2 < 800) return {"Good"};
|
||||
if (eco2 < 1200) return {"Fair"};
|
||||
if (eco2 < 2000) return {"Poor"};
|
||||
return {"Bad"};
|
||||
update_interval: 15s
|
||||
|
||||
# IAQ Classification
|
||||
- platform: template
|
||||
name: "IAQ Classification"
|
||||
lambda: |-
|
||||
int iaq = id(iaq_index).state;
|
||||
if (iaq == 5) return {"Great"};
|
||||
if (iaq == 4) return {"Good"};
|
||||
if (iaq == 3) return {"Fair"};
|
||||
if (iaq == 2) return {"Poor"};
|
||||
return {"Bad"};
|
||||
update_interval: 15s
|
||||
|
||||
# Room Health
|
||||
- platform: template
|
||||
name: "Room Health"
|
||||
id: room_health_text
|
||||
icon: mdi:home-heart
|
||||
lambda: |-
|
||||
float score = id(room_health_score).state;
|
||||
if (score < 0) return {"Unknown"};
|
||||
else if (score >= 90.0) return {"Great"};
|
||||
else if (score >= 80.0) return {"Good"};
|
||||
else if (score >= 60.0) return {"Fair"};
|
||||
else if (score >= 40.0) return {"Poor"};
|
||||
else return {"Bad"};
|
||||
int score = id(room_health).state;
|
||||
if (score == 4) return {"Optimal"};
|
||||
if (score == 3) return {"Fair"};
|
||||
if (score == 2) return {"Poor"};
|
||||
return {"Bad"};
|
||||
update_interval: 15s
|
||||
|
||||
button:
|
||||
@ -1141,100 +1098,34 @@ select:
|
||||
name: "LD2410C Distance Resolution"
|
||||
|
||||
- platform: template
|
||||
name: "Wake Word Selector"
|
||||
id: wake_word_selector
|
||||
options:
|
||||
- "Computer"
|
||||
- "Hey Jarvis"
|
||||
- "Hey Mycroft"
|
||||
- "Okay Nabu"
|
||||
- "Alexa"
|
||||
initial_option: "Computer"
|
||||
name: "Wake word sensitivity"
|
||||
optimistic: true
|
||||
initial_option: Moderately sensitive
|
||||
restore_value: true
|
||||
set_action:
|
||||
# Disable models that aren't selected
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x != "Computer";'
|
||||
then:
|
||||
- micro_wake_word.disable_model: mww_computer
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x != "Hey Jarvis";'
|
||||
then:
|
||||
- micro_wake_word.disable_model: mww_hey_jarvis
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x != "Hey Mycroft";'
|
||||
then:
|
||||
- micro_wake_word.disable_model: mww_hey_mycroft
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x != "Okay Nabu";'
|
||||
then:
|
||||
- micro_wake_word.disable_model: mww_okay_nabu
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x != "Alexa";'
|
||||
then:
|
||||
- micro_wake_word.disable_model: mww_alexa
|
||||
# Enable model we selected
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x == "Computer";'
|
||||
then:
|
||||
- micro_wake_word.enable_model: mww_computer
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x == "Hey Jarvis";'
|
||||
then:
|
||||
- micro_wake_word.enable_model: mww_hey_jarvis
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x == "Hey Mycroft";'
|
||||
then:
|
||||
- micro_wake_word.enable_model: mww_hey_mycroft
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x == "Okay Nabu";'
|
||||
then:
|
||||
- micro_wake_word.enable_model: mww_okay_nabu
|
||||
- if:
|
||||
condition:
|
||||
lambda: 'return x == "Alexa";'
|
||||
then:
|
||||
- micro_wake_word.enable_model: mww_alexa
|
||||
|
||||
- platform: template
|
||||
name: "Wake Word Sensitivity"
|
||||
optimistic: true
|
||||
initial_option: Default
|
||||
restore_value: true
|
||||
entity_category: config
|
||||
options:
|
||||
- Default
|
||||
- More sensitive
|
||||
- Slightly sensitive
|
||||
- Moderately sensitive
|
||||
- Very sensitive
|
||||
set_action:
|
||||
on_value:
|
||||
# Sets specific wake word probabilities computed for each particular model
|
||||
# Note probability cutoffs are set as a quantized uint8 value, each comment has the corresponding floating point cutoff
|
||||
- lambda: |-
|
||||
if (x == "Default") {
|
||||
id(mww_computer).set_probability_cutoff(168); // 0.66 (default)
|
||||
id(mww_hey_jarvis).set_probability_cutoff(247); // 0.97 (default)
|
||||
id(mww_hey_mycroft).set_probability_cutoff(242); // 0.95 (default)
|
||||
id(mww_okay_nabu).set_probability_cutoff(217); // 0.85 (default)
|
||||
id(mww_alexa).set_probability_cutoff(217); // 0.85 (default)
|
||||
} else if (x == "More sensitive") {
|
||||
id(mww_computer).set_probability_cutoff(153); // 0.60
|
||||
id(mww_hey_jarvis).set_probability_cutoff(235); // 0.92
|
||||
id(mww_hey_mycroft).set_probability_cutoff(237); // 0.93
|
||||
id(mww_okay_nabu).set_probability_cutoff(176); // 0.69
|
||||
id(mww_alexa).set_probability_cutoff(176); // 0.69
|
||||
} else if (x == "Very sensitive") {
|
||||
id(mww_computer).set_probability_cutoff(138); // 0.54
|
||||
id(mww_hey_jarvis).set_probability_cutoff(212); // 0.83
|
||||
id(mww_hey_mycroft).set_probability_cutoff(230); // 0.90
|
||||
id(mww_okay_nabu).set_probability_cutoff(143); // 0.56
|
||||
id(mww_alexa).set_probability_cutoff(143); // 0.56
|
||||
}
|
||||
# False Accepts per Hour values are tested against all units and channels from the Dinner Party Corpus.
|
||||
# These cutoffs apply only to the specific models included in the firmware: okay_nabu@20241226.3, hey_jarvis@v2, hey_mycroft@v2
|
||||
lambda: |-
|
||||
if (x == "Slightly sensitive") {
|
||||
id(mww_hey_jarvis).set_probability_cutoff(247); // 0.97 -> 0.563 FAPH on DipCo (Manifest's default)
|
||||
id(mww_hey_mycroft).set_probability_cutoff(253); // 0.99 -> 0.567 FAPH on DipCo
|
||||
id(mww_okay_nabu).set_probability_cutoff(217); // 0.85 -> 0.000 FAPH on DipCo (Manifest's default)
|
||||
id(mww_alexa).set_probability_cutoff(217); // 0.85 -> 0.000 FAPH on DipCo (Manifest's default)
|
||||
} else if (x == "Moderately sensitive") {
|
||||
id(mww_hey_jarvis).set_probability_cutoff(235); // 0.92 -> 0.939 FAPH on DipCo
|
||||
id(mww_hey_mycroft).set_probability_cutoff(242); // 0.95 -> 1.502 FAPH on DipCo (Manifest's default)
|
||||
id(mww_okay_nabu).set_probability_cutoff(176); // 0.69 -> 0.376 FAPH on DipCo
|
||||
id(mww_alexa).set_probability_cutoff(176); // 0.69 -> 0.376 FAPH on DipCo
|
||||
} else if (x == "Very sensitive") {
|
||||
id(mww_hey_jarvis).set_probability_cutoff(212); // 0.83 -> 1.502 FAPH on DipCo
|
||||
id(mww_hey_mycroft).set_probability_cutoff(237); // 0.93 -> 1.878 FAPH on DipCo
|
||||
id(mww_okay_nabu).set_probability_cutoff(143); // 0.56 -> 0.751 FAPH on DipCo
|
||||
id(mww_alexa).set_probability_cutoff(143); // 0.56 -> 0.751 FAPH on DipCo
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user