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Reverse engineering an Arctis Pro Wireless Headset

2021-06-12T00:00:00Z

The Arctis Pro Wireless Headset.

For the last year and a bit, I’ve been using a SteelSeries Arctis Pro Wireless Headset for gaming and talking to friends. It’s a fine headset, but because there’s an always-on receiver there’s no way to detect if the headset is turned on or not from the desktop.

Whenever I start using the headset, I set my desktop’s sound to go to the headset, and then when I stop using the headset I set it to go back to speakers. It doesn’t take more than a second, but some days I might put the headset on a dozen times as I’m on calls, or if it’s noisy outside, etc. That means it’s probably worth at least a few hours of my time trying to automate it.

At first, I hoped I’d be able to tell from the state of the USB device whether there was a headset connected but nothing at all changed when flipping it on and off. Then I went hunting for existing open source tools that might work with it and found that while people have reverse engineered many of the older Arctis headsets, no one has done the same for the Pro Wireless. I finished off with a search to see if anyone had documented the wire protocol even if there was no nice open source software to go with it; I came up short there, too. Looks like I’d have to do it myself.

Capturing data with WireShark

The headset exposes a Human Interface Device (HID), and the wire protocols for earlier versions of the Arctis series looked to be very simple messages passed over the HID connection. It should therefore be fairly easy to use WireShark to capture the data being sent and received by the official SteelSeries application¹.

After setting up WireShark and making it record only the headset’s HID connection, it quickly became apparent that the software was sending three different requests each second, over and over again:

Wireshark, showing a packet capture of a SET_REPORT request from the host to the headset

The packets we’re interested in are the SET_REPORT Request frames sent from the host to the device. Wireshark understands the HID protocol, so it nicely shows us the raw HID data; in the screenshot this is 0x40AA (ignoring the trailing zero bytes). The other requests sent immediately after have 0x41AA and 0x42AA payloads — clearly the first byte is indicating which piece of data is being requested.

The responses to these requests come back in an URB_INTERRUPT in frame:

Wireshark, showing a packet capture of the headset’s response to the earlier request

So the answer to our first request appears to be 0x04. The second response is 0x0402 and the third is 0x04 again. The first thing I tried doing was popping the spare battery out of the charger. The response to the 0x42AA request changed from 0x04 to 0x00 - that’s certainly clear enough! After a bit of waiting around, the battery in my headset dropped from full on the display to three bars, and at that point the 0x40AA response dropped from 0x04 to 0x03. The display on the receiver shows battery state as four bars, and it appears the wire protocol directly corresponds to that particular representation.

That left the 0x41AA request as an unknown. I tried everything I could think of, but it stubbornly kept returning 0x0402. I enlisted a friend who has the same headset to run some hacky Go code and report his values, and he also got a 0x0402 response. As I explained that I couldn’t figure out what these values are, he reported back that turning his headset off made the response change to 0x0202. In all my testing, I’d forgotten to try turning the headset off! That’s the one thing I was actually trying to detect, as well. Thanks, Simon, for helping me get past that bit of stupidity!

I still don’t know what the second byte of the response is, or whether there are other values than 0x04 for on and 0x02 for off, but I’m happy enough to label it as “device status” and move on.

Exploring other features

The software allows you to tweak a bunch of different settings:

SteelSeries Good Game software, showing the advanced headset settings

I went through each one and fiddled with all the values, recording the requests in WireShark as I did so. It turns out the protocol is very simplistic - the wire protocol directly corresponds to the UI elements in the software (or on the receiver, if you navigate through its menus). For example, the software allows you to set the “Headset auto shutoff” value in increments of 10 minutes. You might usually expect this to be converted to seconds or something similar before being passed to the device, but on the wire it’s actually sent as 0x00 for off, 0x01 for 10 minutes, up through to 0x0C for the maximum of 120 minutes.

All the dropdown options seem to function this way — if you pick the 6th option then the request payload will be an 0x06 byte. The sliders have fixed positions they snap to and function similarly: for the two brightness sliders pictured they snap to 11 positions and on the wire these range from 0x00 for off to 0x0A for the maximum.

At this point I was well past my goal of being able to detect whether the headset was on or off, and I was now just trying to see if I could figure out enough of the protocol that I could reimplement the control software on Linux if I ever wanted to. Just as I was about to close the app, something caught my eye: there are integrations with games and other software that can display information on the receiver’s OLED display!

There’s an official API for this, but it involves sending JSON to a webserver which runs as part of their app². That doesn’t feel very nice, and definitely won’t work for me on Linux, so I started a new WireShark session and took some captures while I spoke to myself on Discord.

Decoding pixels

The frames sent to the device whenever Discord showed a notification had a 1060 byte payload. The display on the receiver is 140 pixels wide³ and each pixel can only seem to be on or off, so I figured each bit in the payload corresponded to one bit of the output. I exported the data to binary in the hope that I could visually see what was going on - in theory if I line wrap the data at 140 characters it should look vaguely like the final output. Unfortunately, it did not. There was roughly the right number of high bits, but no amount of fiddling with them in a text editor could give me a coherent picture.

Instead, I wrote some code to write different values to the device. Starting with a payload of all zeroes and gradually increasing a bit at a time every second. After the first few seconds, I saw a line of pixels being drawn downwards on the left of the screen; had I just got the axes the wrong way around? After the first eight pixels lit up, though, they jumped over to the next column. The actual addressing scheme looks something like this:

Diagram of how pixels are addressed on the receiver’s OLED display

So the first 140 bytes given the pixels for the first 8 rows, the next 140 bytes fill in the 8 rows below that, and so on and so forth. Armed with this information I wrote a simple program to read an image and output it to the display:

Headset receiver displaying a custom Hello World message

At this point I was thinking about trying to get Doom rendering on the screen, but I couldn’t find anything nicely hackable that would let me grab the output and pass it on to the receiver. Instead, I decided to try a GIF decoder and before very long had a nice little animated display:

Protocol reference

All the HID messages have a single byte that determines the message type, then an 0xAA byte, then any payload required by the command. These are the ones I’ve figured out:

Byte	Command	Payload
`0x09`	Save changes	None.
`0x10`	Request firmware version(?)	None.
`0x27`	Volume limiter	`0x00` for off, `0x01` for on.
`0x2E`	Equalizer preset	ID of the equalizer preset to use.
`0x39`	Sidetone level	`0x00` for lowest to `0x09` for highest.
`0x3C`	Set headset timeout	Timeout, as a number of 10 minutes. `0x00` for off to `0x0C` for 120 mins.
`0x3E`	Mic mute LED brightness	`0x00` for lowest to `0x0A` for highest.
`0x40`	Request headset battery	None.
`0x41`	Request device status	None.
`0x42`	Request receiver battery	None.
`0x51`	Surround sound mode	`0x00` for off, `0x01` for on.
`0x62`	Auto-start Bluetooth	`0x00` for off, `0x01` for on.
`0x63`	Auto-mute game audio during calls	`0x00` for off, `0x01` for on.
`0x85`	OLED brightness	`0x00` for lowest to `0x0A` for highest.
`0x83`	Equalizer	(Not yet decoded)
`0x89`	Screensaver mode	`0x00` to dim, `0x01` for off, `0x02` for screensaver.
`0xD2`	Render image	Pixel array as described above.

Some of these I’ve not dug too much into because they didn’t seem very interesting. If you have an Arctis Pro Wireless and figure anything more out, let me know, and I’ll update the list.

If you just want to check device state like I originally did, I’ve contributed support for this headset to the excellent HeadsetControl project. It should be in the next release.

Unfortunately (as you’d expect) their application only runs on Windows, so this process involved an annoying amount of rebooting to Windows, fleeing back to Linux, and then realising I hadn’t actually recorded enough to figure it out and repeating. ↩︎
You can also send lisp to the webserver, and it will execute it. No comment. ↩︎
I know this not because it’s mentioned in the technical specs (it’s not), but because I took a photo and counted them out one by one. ↩︎

Debugging beyond the debugger

2019-05-08T00:00:00Z

Real-life debugging tools

Most programming — and sysadmin — problems can be debugged in a fairly straight forward manner using logs, print statements, educated guesses, or an actual debugger. Sometimes, though, the problem is more elusive. There’s a wider box of tricks that can be employed in these cases but I’ve not managed to find a nice overview of them, so here’s mine. I’m mainly focusing on Linux and similar systems, but there tend to be alternatives available for other Operating Systems or VMs if you seek them out.

Networking

tcpdump

tcpdump prints out descriptions of packets on a network interface. You can apply filters to limit which packets are displayed, chose to dump the entire content of the packet, and so forth.

Typical usage might look something like:

# tcpdump -nSi eth0 port 80
tcpdump: verbose output suppressed, use -v or -vv for full protocol decode
listening on eth0, link-type EN10MB (Ethernet), capture size 262144 bytes
16:03:35.577781 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [S], seq 2815779044, win 64800, options [mss 1440,sackOK,TS val 2378811665 ecr 0,nop,wscale 7], length 0
16:03:35.586853 IP6 2001:db8::2.80 > 2001:db8::1.54742: Flags [S.], seq 1522609102, ack 2815779045, win 28560, options [mss 1440,sackOK,TS val 3063610173 ecr 2378811665,nop,wscale 7], length 0
16:03:35.586877 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [.], ack 1522609103, win 507, options [nop,nop,TS val 2378811674 ecr 3063610173], length 0
16:03:35.620678 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [P.], seq 2815779045:2815779399, ack 1522609103, win 507, options [nop,nop,TS val 2378811708 ecr 3063610173], length 354: HTTP: GET / HTTP/1.1

Here you can see the start of a plaintext HTTP request: the three-way handshake as the TCP connection is established followed by a GET request. Even if the data is encrypted as it will be in most cases, it’s often useful to see the “shape” of the transmissions: did the client start sending data when it connected, did the server ever respond, etc.

Daniel Miessler has a good tutorial on tcpdump if you’re not familiar with it and don’t want to jump straight into the man page.

… with Docker

Docker sets up separate network namespaces for each container. To see the traffic across the interfaces of a single container you can nsenter the container’s network namespace:

# nsenter -t $(docker inspect --format '{{.State.Pid}}' my_container) -n tcpdump -nS port 80

This retrieves the PID for the container, and tells nsenter to enter the network (-n) namespace from the given target (-t) PID, and then run the given command (in this case tcpdump ...).

openssl s_client / s_server

When a connection is using TLS it’s often useful to try connecting to the server and see what certificate it presents, algorithms it negotiates, and so forth. OpenSSL offers two useful subcommands which can help with this: s_client for connecting as a client, and s_server for listening to connections.

For example, using s_client to connect to google.com on the standard HTTPS port shows us details about the server cert and its verification status:

$ openssl s_client -connect google.com:443
CONNECTED(00000003)
depth=2 OU = GlobalSign Root CA - R2, O = GlobalSign, CN = GlobalSign
verify return:1
depth=1 C = US, O = Google Trust Services, CN = Google Internet Authority G3
verify return:1
depth=0 C = US, ST = California, L = Mountain View, O = Google LLC, CN = *.google.com
verify return:1
---
Certificate chain
 0 s:C = US, ST = California, L = Mountain View, O = Google LLC, CN = *.google.com
   i:C = US, O = Google Trust Services, CN = Google Internet Authority G3
 1 s:C = US, O = Google Trust Services, CN = Google Internet Authority G3
   i:OU = GlobalSign Root CA - R2, O = GlobalSign, CN = GlobalSign
---
# ...

Whereas connecting to my webserver and providing an unknown host in the SNI field results in an SSL alert 112 (“The server name sent was not recognized”) and no server certificate is sent:

$ openssl s_client -connect chameth.com:443 -servername example.com
CONNECTED(00000003)
140384831313024:error:14094458:SSL routines:ssl3_read_bytes:tlsv1 unrecognized name:../ssl/record/rec_layer_s3.c:1536:SSL alert number 112
---
no peer certificate available
---
# ...

Often if you hit this kind of alert in an application the exact error will be lost somewhere in the many layers between the SSL library and the logs, so being able to directly connect and test can help diagnose a lot of issues.

Once a connection is established you can read and write plain text and it will be encrypted and decrypted automatically.

Java apps

If a Java app is involved in the connection, you can enable a lot of built-in debugging with a simple JVM property: javax.net.debug. You can tweak what exactly gets logged, but the easiest thing to do is just set the property to all and you’ll see information about certificate chains, verification, and packet dumps:

$ java -Djavax.net.debug=all -jar ....
# ...
found key for : duke
chain [0] = [
[
  Version: V1
  Subject: CN=Duke, OU=Java Software, O="Sun Microsystems, Inc.",
  L=Cupertino, ST=CA, C=US
# ...

More information about Java’s debugging options is available on docs.oracle.com.

Thread and core dumps

Higher-level languages frequently provide an interactive way to dump the current execution state of all of their threads (a “thread dump”). This is useful to spot deadlocks, some types of race conditions, and as a quick and dirty method of investigating hangs or excessive CPU usage.

With both Java and Go applications you can send a QUIT signal to have a thread dump printed out; Go applications will quit after doing so, Java ones will carry on running. At most terminals you can hit Ctrl and \ to send a QUIT signal.

For Java you can also use the jstack tool from the JDK to dump threads by PID; this can be useful if the application is running in the background or has redirected sysout:

$ jstack 8321
Attaching to process ID 8321, please wait...
Debugger attached successfully.
Client compiler detected.

Thread t@5: (state = BLOCKED)
 - java.lang.Object.wait(long) @bci=-1107318896 (Interpreted frame)
 - java.lang.Object.wait(long) @bci=0 (Interpreted frame)
 - java.lang.ref.ReferenceQueue.remove(long) @bci=44, line=116 (Interpreted frame)
 - java.lang.ref.ReferenceQueue.remove() @bci=2, line=132 (Interpreted frame)
 - java.lang.ref.Finalizer$FinalizerThread.run() @bci=3, line=159 (Interpreted frame)

# ...

A core dump provides more complete information about the state of a process, but is often more complex to interpret. The gcore utility from GDB will create a core dump of a process with a given PID. You can then generally load the core file using your normal debugger, depending on the language in question.

System calls

strace is the swiss army knife for seeing what a process is doing. It details each system call made by a program (you can filter them down, of course). For example:

$ strace -e read curl https://google.com/
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0 \236\0\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0P!\0\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\200l\2\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\20Q\0\0\0\0\0\0"..., 832) = 832
# ...
read(3, "\0\0\0\0\0\0\0\4\25\345\366\302\273sE6\365wI\225\321|\3435Z\362\216\372\215\251aO"..., 253) = 253
<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>301 Moved</TITLE></HEAD><BODY>
<H1>301 Moved</H1>
The document has moved
<A HREF="https://www.google.com/">here</A>.
</BODY></HTML>
read(3, "\27\3\3\0!", 5)                = 5
# ...

Brendan Gregg has a nice guide on strace and alternatives.

… with docker

When the application is running in docker you can usually just strace it from the host with the correct PID (from e.g. docker inspect --format '{{.State.Pid}}' my_container). Sometimes you may need to trace the startup of an application though, which is a bit trickier. Instead you can run a new container using the same PID namespace as your target, and the permissions needed to strace:

$ docker run --rm -it --pid=container:my_container \
  --net=container:my_container \
  --cap-add sys_admin \
  --cap-add sys_ptrace \
  alpine

From within the new container you can install strace, and trace any running program within the target container using strace -p as normal. To start a new program you need access to the target container’s filesystem, which you can get to via /proc/1/root (PID 1 being the main process that docker started in the target container).

Files

Sometimes the problem might relate to file access. There are a couple of straight forward — but nonetheless useful — tools which might help here. inotifywait uses the Linux inotify subsystem to watch files or directories for operations. For example:

$ inotifywait -mr site/content
Setting up watches.  Beware: since -r was given, this may take a while!
Watches established.
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ OPEN 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ CLOSE_WRITE,CLOSE 2019-05-08-debugging-beyond-the-debugger.md
# ...

Here the -m switch makes inotifywait monitor the files forever (instead of exiting on the first modification, which is the normal behaviour) and r makes it recurse into the directory and monitor each file and subdirectory in there.

If you want to see what processes currently have a file open, fuser is the go-to tool. For example:

$ fuser -v /
                     USER PID ACCESS COMMAND
/:                   root     kernel mount /
                     chris      2961 .rc.. systemd
                     chris      2986 .r... gdm-x-session
                     chris      2994 .r... dbus-daemon
                     chris      3001 .r... gnome-session-b
# ...

Honourable mentions

These aren’t really debugging tools, but I feel it’s worth mentioning as they often feature somewhere along the debugging-of-weird-problems journey.

I’ve seen some weird and wonderful problems happen because a disk is full, so a quick df early on in the debugging process never hurts. Some apps may hang, some may corrupt their config, some may fall over and die; sometimes the manner in which they fail doesn’t obviously point to a disk space issue.

Another issue that comes up now and then — especially inside VMs or other environment that don’t have a decent amount of “noise” happening — is entropy exhaustion. A quick look at /proc/sys/kernel/random/entropy_avail should be enough to confirm that everything is ticking along nicely. If it’s exceedingly low then you may find that anything involving random number generation stalls (TLS connections for example).

Reverse engineering the Sense API

2016-04-10T00:00:00Z

A Sense unit and its two pillow sensors

Sense is a little device that sits by your bedside and, in conjunction with a little ‘pill’ attached to your pillow, monitors your sleeping patterns and any environmental conditions that might hamper them. Android and iOS apps show you your sleep history, and offer suggestions for improvements.

Sense was Kickstarted in August 2014, raising over 2.4 million US dollars, and shipped to backers in mid 2015. The campaign blurb included this snippet:

Building with Sense

You’ll always have access to your data via our API. Take it, play with it, graph it, do whatever you want with it. It’s yours. That’s important to us.

We enjoy tinkering with and building on-top of other products we like. Sense will let you have that experience.

We’d love to hear your thoughts on what you might want to build with Sense, and how you could directly interact with the hardware, and the data it collects.

Sounds great! But a year after shipping, there’s no sign of an API, and some of us who enjoy tinkering are getting a bit restless…

Reverse engineering to the rescue

Data from the Sense hardware is transmitted off onto the Internet somewhere, and then pulled down by the mobile apps. If we can snoop that traffic, we can probably figure out how to grab the data from the Sense.

So the first step is to capture the raw network traffic while the Sense and the app are in use. I crafted an extremely over-the-top setup that involves my phone and the Sense being on a new, separate wireless network that gateways through a Linux server that can look in on all the traffic (because you never know when a dedicated capturing network will come in handy, right?…)

Running tshark while the app and device are active showed they were making HTTPS requests an Amazon Elastic Cloud instance. That’s not really much use, as we can’t see the content of the requests. The next step is to move up the network stack and target the HTTPS traffic specifically. Step in, mitmproxy. This automates man-in-the-middle attacks on HTTPS requests. When it receives such a request, it cooks up its own certificate and sends that to the client, and then sits in between while the client and the server communicate.

The certificates generated by mitmproxy aren’t issued by a trusted source, so (well behaved) browsers and apps will throw up lots of warnings and refuse to communicate with it for the most part. This is the mechanism that stops anyone impersonating your banking website when you access it over HTTPS, so is generally a very good thing. In this case, both the Sense itself and the Android app refused to talk with the blatant attacker.

We control the Android phone, though, so can just tell it that we trust the dodgy certificate issuer. This involves grabbing the certificate generated by mitmproxy, and adding it to Android’s key store. The process is documented nicely in the mitmproxy docs and is pretty straightforward. With Android trusting our certificate authority, the Sense app starts talking through the proxy and we can look at the plain HTTP requests.

The API

Logging in to the Android app and navigating through the various screens shows that the API is actually pretty nice. It’s RESTful, and uses an OAuth bearer token for authorisation, as you can see in the mitmproxy output of the authentication and first few requests:

POST https://api.hello.is/v1/oauth2/token
    ← 200 application/json 151B 1.17s
GET https://api.hello.is/v2/account/preferences
    ← 200 application/json 124B 109ms
GET https://api.hello.is/v1/account
    ← 200 application/json 181B 388ms
GET https://api.hello.is/v2/devices
    ← 200 application/json 235B 126ms
POST https://api.hello.is/v1/app/checkin
    ← 200 application/json 106B 119ms
GET https://api.hello.is/v2/insights
    ← 200 application/json 1.4kB 157ms
POST https://api.hello.is/v1/notifications/registration
    ← 204 [no content] 364ms
GET https://api.hello.is/v2/timeline/2016-04-09
    ← 200 application/json 160B 550ms
GET https://api.hello.is/v1/questions?date=2016-04-10
    ← 200 application/json 284B 132ms
PATCH https://api.hello.is/v1/app/stats
    ← 202 [no content] 126ms
GET https://api.hello.is/v1/app/stats/unread
    ← 200 application/json 71B 171ms

Authorisation

To get a bearer token, you send a POST request to /v1/oauth2/token, supplying the username and password, and a client ID and secret. Presumably if the API is ever opened up you’ll be able to register clients and get your own ID and secret, but for now reusing the ones from the Android app works fine. [I’ve prettified and linewrapped the content to make it a bit easier to read.]

POST /v1/oauth2/token HTTP/1.1
Host: api.hello.is
Accept: */*
Content-Length: 123
Content-Type: application/x-www-form-urlencoded

grant_type=password
 &client_id=8d3c1664-05ae-47e4-bcdb-477489590aa4
 &client_secret=4f771f6f-5c10-4104-bbc6-3333f5b11bf9
 &username=USERNAME
 &password=PASSWORD

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:25:16 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "token_type":"Bearer",
  "expires_in":31536000,
  "account_id":"1234",
  "access_token":"2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  "refresh_token":"2.yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy"
}

Sleep timeline

Getting the timeline for one night is just a straight up GET request. It gives a detailed log of events, including different ‘depths’ of sleep (I’ve cut out a whole bunch here for simplicity). It also shows metrics relating to the entire night, and their condition (‘warning’ or ‘ideal’).

GET /v2/timeline/2016-04-08 HTTP/1.1
Host: api.hello.is
Accept: */*
Authorization: Bearer 2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:29:00 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "score":76,
  "score_condition":"WARNING",
  "message":"You were asleep for **8.2 hours**, and sleeping soundly for 3.2 hours.",
  "date":"2016-04-08",
  "events":[
    {
      "timestamp":1460168700000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You went to bed.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"GOT_IN_BED",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460169600000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You fell asleep.",
      "sleep_depth":100,
      "sleep_state":"SOUND",
      "event_type":"FELL_ASLEEP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460177040000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You were moving around quite a bit.",
      "sleep_depth":2,
      "sleep_state":"AWAKE",
      "event_type":"GENERIC_MOTION",
      "valid_actions":["VERIFY","INCORRECT"]
    },{
      "timestamp":1460199000000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"Good morning.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"WOKE_UP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    }
  ],
  "metrics":[
    { "name":"total_sleep", "value":490, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"sound_sleep", "value":190, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"time_to_sleep", "value":15, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"times_awake", "value":2, "unit":"QUANTITY", "condition":"IDEAL" },
    { "name":"fell_asleep", "value":1460169600000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"woke_up", "value":1460199000000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"temperature", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"humidity", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"particulates", "value":null, "unit":"CONDITION", "condition":"WARNING" },
    { "name":"light", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"sound", "value":null, "unit":"CONDITION", "condition":"IDEAL"}
  ]
}

Other interesting resources

Pretty much everything displayed in the app is available as a REST resource. They clearly put some thought into API design, which makes it even more of a shame that they haven’t made it public yet. Some of the other interesting resources are:

/v1/alarms: the alarms set on the Sense.
/v1/room/current?temp_unit=c: gives the latest environmental readouts from the Sense.
/v1/room/all_sensors/hours?quantity=2&from_utc=<timestamp>: sensor history.
/v2/devices: the paired devices, including battery status of pills.
/v2/insights: JSON representation of the cards displayed on the main screen (recommendations, analysis, etc).
/v2/trends/LAST_WEEK (or MONTH or 3_MONTHS): gives graph data for sleep scores, duration and depths.

Now the API is figured out, all that’s left is to build something that uses it…