diff --git a/config/stat-collector-crontab b/config/stat-collector-crontab
new file mode 100755
index 000000000..d8a9cf261
--- /dev/null
+++ b/config/stat-collector-crontab
@@ -0,0 +1,3 @@
+# Automatically collects stats every 5 minutes for the stat-collector service
+# Installed by AmpliPi
+*/5 * * * * /usr/local/bin/stat-collector.py
diff --git a/scripts/configure.py b/scripts/configure.py
index 69ccb1c36..a2b73a85c 100755
--- a/scripts/configure.py
+++ b/scripts/configure.py
@@ -207,6 +207,20 @@
             'popd',
         ]
     },
+    'stat-collector': {
+        "copy": [
+          {
+            'from': 'config/stat-collector-crontab',
+            'to': '/etc/cron.d/stat-collector',
+            'sudo': 'true',
+          },
+          {
+            'from': 'scripts/stat-collector.py',
+            'to': '/usr/local/bin/stat-collector.py',
+            'sudo': 'true',
+          },
+        ],
+    },
     # streams
     # TODO: can stream dependencies be aggregated from the streams themselves?
     'airplay': {
diff --git a/scripts/stat-collector.py b/scripts/stat-collector.py
new file mode 100644
index 000000000..6a4a201c9
--- /dev/null
+++ b/scripts/stat-collector.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+import os
+import json
+import subprocess
+from datetime import datetime, timezone, timedelta
+from shutil import disk_usage
+import requests
+# pylint: disable=no-name-in-module
+from pydantic import BaseModel
+from psutil import virtual_memory
+
+
+def find_matches(list1: list, list2: list):
+  """Takes in two lists, returns a list that only contains data shared by both lists"""
+  set1 = {json.dumps(item, sort_keys=True) for item in list1}
+  set2 = {json.dumps(item, sort_keys=True) for item in list2}
+
+  matches = [json.loads(item) for item in set1 & set2]
+  return matches
+
+
+def average(running_average, weight, new_entry):
+  """
+    Calculates an average using three variables:
+    running_average, the average so far
+    weight, the count of numbers that went into that average
+    new_entry, the newest addition to the dataset
+    Returns the new average
+  """
+  # Round to nearest whole number, then int() to shave of trailing .0
+  return int(round(((running_average * weight) + new_entry) / (weight + 1), 0))
+
+
+class StreamUsageSchema(BaseModel):
+  """Schema for individual stream type usage statistics"""
+  # For the purpose of the below comments, a "survey cycle" is defined as how often the device phones home
+  active_streams: list = []
+
+  # The highest number of concurrent streams running within the survey cycle
+  average_concurrent_streams: int = 0
+  peak_concurrent_streams: int = 0
+
+  # The length in seconds that a stream of this type has been connected within the survey cycle
+  # Counts as long as at least one playing stream of the given type persists between polling calls
+  current_runtime: int = 0
+  average_runtime: int = 0
+  runtime_weight: int = 0
+  peak_runtime: int = 0
+
+
+  def consume(self, source_info: list, poll_count: int, timediff: timedelta):
+    """Consumes a list of source["info"] json blobs and updates stream statistics tracker with information found within"""
+    stream_names = [item["name"] for item in source_info]
+    stream_count = len(stream_names)
+
+    if stream_count == 0 and len(self.active_streams) != 0:  # If a stream has just been shut off, record average based on current runtime
+      new_average = average(self.average_runtime, self.runtime_weight, self.current_runtime)
+      self.average_runtime = new_average
+      self.runtime_weight += 1
+
+    matches = find_matches(self.active_streams, stream_names)
+    if len(matches) != 0:  # If there is at least one playing match, calculate runtime
+      self.current_runtime = self.current_runtime + timediff.seconds
+
+      self.peak_runtime = max(self.peak_runtime, self.current_runtime) if self.current_runtime < 86400 else 86400
+    else:
+      self.current_runtime = 0
+
+    self.peak_concurrent_streams = max(self.peak_concurrent_streams, stream_count)
+    self.average_concurrent_streams = average(self.average_concurrent_streams, poll_count, stream_count)
+
+    # set current data to be next poll's previous data
+    self.active_streams = stream_names
+
+
+class UsageSurveySchema(BaseModel):
+  """A verification layer for JSON objects to be sent to the Usage Survey server"""
+  # Auth code that legitimizes the data as coming from a MicroNova AmpliPro and not some random other source
+  authentication_code: str = os.environ.get('AUTH_CODE', "")
+  amplipi_version: str = ""
+  is_streamer: bool = False
+
+  poll_count: int = 0  # Used to calculate averaged values
+  time_of_first_poll: datetime = datetime.min
+  time_of_previous_poll: datetime = datetime.min
+
+  # Memory measured in Kb
+  peak_memory_usage: int = 0
+  average_memory_usage: int = 0
+
+  # Disk space measured in Gb
+  # Disk average not collected as it's less variable over time
+  disk_total: int = 0
+  disk_used: int = 0
+  disk_free: int = 0
+
+  # Used to store notable events, such as anything that occurs above a logging level of warning. See record_logs() function for details.
+  notable_logs: list[str] = []
+
+  # These were once in a dict called "streams" that contained the data in the same format, having there not be dicts 3 layers deep seemed prefferable
+  airplay: StreamUsageSchema = StreamUsageSchema()
+  aux: StreamUsageSchema = StreamUsageSchema()
+  bluetooth: StreamUsageSchema = StreamUsageSchema()
+  dlna: StreamUsageSchema = StreamUsageSchema()
+  fileplayer: StreamUsageSchema = StreamUsageSchema()
+  internetradio: StreamUsageSchema = StreamUsageSchema()
+  lms: StreamUsageSchema = StreamUsageSchema()
+  media_device: StreamUsageSchema = StreamUsageSchema()
+  pandora: StreamUsageSchema = StreamUsageSchema()
+  plexamp: StreamUsageSchema = StreamUsageSchema()
+  rca: StreamUsageSchema = StreamUsageSchema()
+  spotify: StreamUsageSchema = StreamUsageSchema()
+
+  def save_to_disk(self, path="/var/lib/UsageSurvey.json"):
+    """Saves contents of UsageSurvey to file"""
+    tmp = "/tmp/UsageSurvey.json"
+    with open(tmp, "w", encoding="UTF-8") as file:
+      file.write(self.json())
+    subprocess.run(['sudo', 'mv', tmp, path], check=True)
+
+  @classmethod
+  def load_from_disk(cls, path="/var/lib/UsageSurvey.json"):
+    """Loads contents of UsageSurvey from saved file"""
+    if os.path.exists(path):
+      with open(path, "r", encoding="UTF-8") as file:
+        file_data = json.load(file)
+        return cls(**file_data)
+    else:
+      return cls()
+
+  def get_disk_usage(self):
+    """Collects and populates disk usage statistics via shutil"""
+    self.disk_total, self.disk_used, self.disk_free = disk_usage("/")
+
+  def get_mem_usage(self):
+    """Collects and populates memory usage statistics via psutil"""
+    memory_info = virtual_memory()
+    used_memory = memory_info.used
+
+    self.peak_memory_usage = max(self.peak_memory_usage, used_memory)
+    self.average_memory_usage = average(self.average_memory_usage, self.poll_count, used_memory)
+
+  def record_logs(self):
+    """Reads journalctl and searches for a list of keywords, then saves new logs to self.notable_logs"""
+    keywords = ["WARNING", "ERROR"]
+
+    result = subprocess.run(
+        ["journalctl", "--no-pager", f"--grep={'|'.join(keywords)}"],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+        check=True
+    )
+
+    # Split logs into individual line items in a list, then add new entries to the notable_logs list
+    logs = result.stdout.splitlines()
+    self.notable_logs.extend([log for log in logs if log not in self.notable_logs])
+
+
+  def get_state(self):
+    """Gets system state, saves to relevant places"""
+    state = requests.get("http://localhost/api", timeout=1)
+    if state.status_code == 200:
+      state_json = state.json()
+      now = datetime.now(timezone.utc)  # in UTC to avoid any issues if we do implement a user-set timezone option
+      if self.time_of_first_poll == datetime.min:
+        self.time_of_first_poll = now
+
+      timediff = (now - self.time_of_previous_poll) if self.time_of_previous_poll != datetime.min else timedelta(0)
+      stream_handlers = {
+        "airplay": {"object": self.airplay, "list": []},
+        "aux": {"object": self.aux, "list": []},
+        "bluetooth": {"object": self.bluetooth, "list": []},
+        "dlna": {"object": self.dlna, "list": []},
+        "fileplayer": {"object": self.fileplayer, "list": []},
+        "internetradio": {"object": self.internetradio, "list": []},
+        "lms": {"object": self.lms, "list": []},
+        "media_device": {"object": self.media_device, "list": []},
+        "pandora": {"object": self.pandora, "list": []},
+        "plexamp": {"object": self.plexamp, "list": []},
+        "rca": {"object": self.rca, "list": []},
+        "spotify": {"object": self.spotify, "list": []},
+      }
+
+      for source in state_json["sources"]:
+        if source["input"] and source.get("info", {}).get("state") in ["playing", "connected"]:
+          stream_type = source["info"]["type"]
+          if stream_type in stream_handlers:
+            stream_handlers[stream_type]["list"].append(source["info"])
+
+      for key, handler in stream_handlers.items():
+        print(key)
+        handler["object"].consume(handler["list"], self.poll_count, timediff)
+
+      self.poll_count += 1
+      self.time_of_previous_poll = now
+      self.amplipi_version = state_json["info"]["version"]
+      self.is_streamer = state_json["info"]["is_streamer"]
+      self.get_disk_usage()
+      self.get_mem_usage()
+      self.record_logs()
+
+
+if __name__ == "__main__":
+  UsageSurvey = UsageSurveySchema.load_from_disk()
+  UsageSurvey.get_state()
+  UsageSurvey.save_to_disk()