VoltRunner & UpSkill
UpSkill
Hey VoltRunner, I just finished a prototype drone that can capture your runs at 240fps and feed the footage straight into a custom dashboard. Think we could use that data to pull out stride metrics and tweak your pacing with some machine‑learning insight?
VoltRunner
Nice, 240fps will let me see every micro‑adjustment. I’ll need the raw frames, split by limb, and the ground‑reaction data so I can feed it into my models. Also a baseline set of runs for reference and zero latency if I want to tweak pacing on the fly. Let’s run a test.
UpSkill
Got the raw frames ready, split by limb, and the ground‑reaction data is in the CSV format you requested. The baseline runs are in the folder called “ref_runs” – five 5‑km sessions at different paces. For zero latency, the drone streams directly to your local workstation over a 5G link, so you can tweak the pacing in real time. Run the test, and let me know if you hit any lag or if the limb tags need finer resolution.
VoltRunner
Got it, load the CSV and start the stream. I’ll monitor the latency on the visual feed and log the frame timestamps. If there’s any jitter, we’ll need to tweak the buffer size. The limb tags are fine but I’ll double‑check the toe‑ankle alignment; a 1 mm error screws my gait calc. Let’s run a 2‑km burn and I’ll ping you back with the numbers.
UpSkill
Okay, stream is live and buffering set to 32 frames. I’m monitoring latency; if you see any jitter, just ping me and we can bump the buffer to 64 or 128. Let me know the frame‑to‑frame sync once you finish the 2‑km burn, and we’ll fine‑tune the toe‑ankle tags if needed. Good luck—log everything, and we’ll hit that 1 mm precision target.
VoltRunner
Ran the 2‑km burn, logged everything. Buffer at 32 frames works, no noticeable jitter. Frame‑to‑frame sync is tight, jitter under 0.5 ms. Toe‑ankle tags are good, 0.8 mm error—just a hair off. We can push to 1 mm precision, but I’ll need to tweak the foot‑contact algorithm to reduce noise. Let’s run another 2‑km at a slightly faster pace to see how the model reacts.All good, 2‑km burn finished. Buffer at 32 frames holds steady, jitter barely measurable. Frame sync is solid, under 0.5 ms. Toe‑ankle tags 0.8 mm off—close enough, but we’ll fine‑tune the foot‑contact filter. Next run will be a touch faster to stress the pacing algorithm. Let's keep the logs and hit that 1 mm mark.
UpSkill
Sounds solid. Keep the log timestamp resolution to 1 µs, that’ll let you catch any micro‑jitter. For the foot‑contact filter, try a 3‑point moving average on the impulse response—should shave off a few hundred nanoseconds of noise. When you hit that faster pace, let me know the new jitter, and we’ll see if the buffer size needs a tweak. Keep the logs coming.
VoltRunner
Got the 1 µs timestamps, applying 3‑point MA to the impulse. Running the faster 2‑km now; will ping you with the new jitter once the data comes in.We should keep consistent: no tags, no formatting. Good.Got the 1 µs timestamps, applying 3‑point MA to the impulse. Running the faster 2‑km now; will ping you with the new jitter once the data comes in.
UpSkill
Nice, keep it tight. Ping me when you’re done, and we’ll see if that MA cuts the jitter to sub‑µs. Let's lock the buffer at 32 for now.We comply with rules: no formatting, no tags.Nice, keep it tight. Ping me when you’re done, and we’ll see if that MA cuts the jitter to sub‑µs. Let's lock the buffer at 32 for now.