开发活动追踪器：静止时自动暂停GPS追踪及省电可行性咨询

Hey Chris, great question—this is a super common optimization for activity trackers, especially given your use case with long pauses between movement bursts. Let’s break this down for you:

The core here is combining motion detection with smart GPS management. Here’s how to implement it:

• First, reliably detect stationary state
  Use the device’s accelerometer as your primary trigger: set a threshold (e.g., <0.1g of acceleration) and check if the user’s motion stays below this for a consistent window (like 30 seconds). You can cross-verify with GPS speed data (if available) — if the reported speed stays below 1 km/h for the same window, that confirms the user is stationary. This avoids false positives from small movements (like adjusting a bag or checking a phone).
• Pause GPS updates strategically
  Once stationary is confirmed, don’t just kill GPS entirely (unless you’re sure the pause will be very long). Instead:
  • On Android: Use FusedLocationProviderClient to switch to an extremely low update interval (e.g., 5 minutes) or call removeLocationUpdates() to stop active tracking. You can keep a passive location provider as a fallback if needed.
  • On iOS: Call stopUpdatingLocation() on CLLocationManager, but consider enabling startMonitoringSignificantLocationChanges() to wake up tracking if the user moves far enough (this uses way less power).
• Auto-resume tracking seamlessly
  When the accelerometer detects motion above your threshold (e.g., >0.3g) or GPS (if you kept low-frequency updates) picks up a speed increase, immediately switch back to your normal GPS update rate (e.g., 1-5 seconds interval). Add a small delay (like 2-3 seconds) to avoid triggering on tiny, non-movement actions.
• Handle edge cases
  In areas with poor GPS signal, rely more on the accelerometer to avoid false stationary flags. Also, make sure your code can handle sudden transitions (e.g., the user jumps into a car after a long pause) without lag.

Absolutely — this is one of the most impactful battery optimizations you can make for a tracking app.
GPS modules draw significant power when actively polling for location fixes. By pausing or reducing update frequency during long stationary periods, you let the GPS hardware enter low-power mode or even shut down entirely. For your 60-120 minute pauses, this could cut GPS-related battery usage by 90% or more during those windows.
To amplify savings:

• Use the accelerometer in low-power mode (e.g., 1Hz sampling rate) instead of high-frequency sampling when monitoring for motion.
• Disable other non-essential sensors (like gyroscopes) during stationary periods.

Since you care about clean, accurate polylines, here’s what to keep in mind:

• Cache key location points
  When entering stationary mode, save the last valid GPS location (with high accuracy). When resuming tracking, save the first new valid location. Your polyline will then show a clean "pause" (either a single point or a short line between the end of movement and start of next movement) instead of a cluster of redundant points.
• Avoid trajectory gaps
  Ensure the cached end point and the first resumed point are properly connected in your polyline data. Don’t skip either — this keeps the trajectory looking continuous and logical.
• Wait for high-accuracy fixes on resume
  When restarting GPS, ignore the first few low-accuracy fixes (e.g., <10m accuracy) until you get a reliable location. This prevents messy, inaccurate points from messing up your polyline.

内容的提问来源于stack exchange，提问作者Chris_1983_Norway