Other
LiteRT
LiteRT
mobility
mobility-model-zoo
condition-monitoring
activity
experimental
tinyml
microcontroller
ESP32
time-series-classification
imu
activity-recognition
int8
Eval Results (legacy)
Instructions to use mobility-model-zoo/pace-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use mobility-model-zoo/pace-cnn with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download config.json from mobility-model-zoo/pace-cnn: direct link, hf CLI and curl.
- Browser
- Download file 3.74 kB
-
https://huggingface.co/mobility-model-zoo/pace-cnn/resolve/main/config.json
- Command line
-
hf download hf://mobility-model-zoo/pace-cnn/config.json
-
curl -L -o config.json https://huggingface.co/mobility-model-zoo/pace-cnn/resolve/main/config.json
3.74 kB
| { | |
| "model": "pace-cnn", | |
| "version": "0.1.0", | |
| "format": "pace-cnn-v1", | |
| "labels": [ | |
| "walking", | |
| "walking_upstairs", | |
| "walking_downstairs", | |
| "sitting", | |
| "standing", | |
| "laying" | |
| ], | |
| "input": { | |
| "shape": [ | |
| 1, | |
| 128, | |
| 9 | |
| ], | |
| "layout": "row-major: sample t, then channel c (index t * 9 + c)", | |
| "window": 128, | |
| "rate_hz": 50, | |
| "overlap": 0.5, | |
| "channels": [ | |
| { | |
| "name": "body_acc_x", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| }, | |
| { | |
| "name": "body_acc_y", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| }, | |
| { | |
| "name": "body_acc_z", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| }, | |
| { | |
| "name": "body_gyro_x", | |
| "unit": "rad/s" | |
| }, | |
| { | |
| "name": "body_gyro_y", | |
| "unit": "rad/s" | |
| }, | |
| { | |
| "name": "body_gyro_z", | |
| "unit": "rad/s" | |
| }, | |
| { | |
| "name": "total_acc_x", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| }, | |
| { | |
| "name": "total_acc_y", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| }, | |
| { | |
| "name": "total_acc_z", | |
| "unit": "g (1 g = 9.80665 m/s^2)" | |
| } | |
| ], | |
| "sensor": "6-axis IMU worn at the waist (belt), as in UCI HAR" | |
| }, | |
| "normalisation": { | |
| "rule": "(x - mean) / std per channel", | |
| "from": "training subjects of uci-har-v1 (validation excluded)", | |
| "mean": [ | |
| -0.0004951871815137565, | |
| -0.00032817694591358304, | |
| -0.00021286075934767723, | |
| 0.0005902921548113227, | |
| -0.0004714796377811581, | |
| -0.00026790332049131393, | |
| 0.8028175234794617, | |
| 0.02752470225095749, | |
| 0.081498883664608 | |
| ], | |
| "std": [ | |
| 0.19266846776008606, | |
| 0.1205918937921524, | |
| 0.10867609083652496, | |
| 0.4066708981990814, | |
| 0.3769063949584961, | |
| 0.25676658749580383, | |
| 0.410501092672348, | |
| 0.382828027009964, | |
| 0.373470276594162 | |
| ] | |
| }, | |
| "quantisation": { | |
| "input_scale": 0.060373175889253616, | |
| "input_zero_point": -1, | |
| "output_scale": 0.06330011039972305, | |
| "output_zero_point": -19, | |
| "output": "int8 scores in label order; argmax is the predicted activity" | |
| }, | |
| "preparation": { | |
| "rate_hz": 50, | |
| "window": 128, | |
| "step": 64, | |
| "units": { | |
| "acc": "g (1 g = 9.80665 m/s^2)", | |
| "gyro": "rad/s" | |
| }, | |
| "channels": [ | |
| "body_acc_x", | |
| "body_acc_y", | |
| "body_acc_z", | |
| "body_gyro_x", | |
| "body_gyro_y", | |
| "body_gyro_z", | |
| "total_acc_x", | |
| "total_acc_y", | |
| "total_acc_z" | |
| ], | |
| "steps": [ | |
| { | |
| "op": "median", | |
| "length": 3, | |
| "causal": true, | |
| "on": "acc and gyro, per axis" | |
| }, | |
| { | |
| "op": "butterworth_lowpass", | |
| "order": 3, | |
| "cutoff_hz": 20.0, | |
| "on": "acc and gyro, per axis", | |
| "sos": [ | |
| [ | |
| 0.6990599365895495, | |
| 1.398119873179099, | |
| 0.6990599365895495, | |
| 1.0, | |
| 1.25051643084874, | |
| 0.5457233155094577 | |
| ], | |
| [ | |
| 0.7547627247472144, | |
| 0.7547627247472144, | |
| 0.0, | |
| 1.0, | |
| 0.5095254494944288, | |
| 0.0 | |
| ] | |
| ] | |
| }, | |
| { | |
| "op": "gravity_separation", | |
| "lowpass": { | |
| "type": "butterworth", | |
| "order": 3, | |
| "cutoff_hz": 0.3, | |
| "sos": [ | |
| [ | |
| 0.0003486925369977911, | |
| 0.0006973850739955822, | |
| 0.0003486925369977911, | |
| 1.0, | |
| -1.9616121845004821, | |
| 0.9630069546484733 | |
| ], | |
| [ | |
| 0.0185029745248923, | |
| 0.0185029745248923, | |
| 0.0, | |
| 1.0, | |
| -0.9629940509502154, | |
| 0.0 | |
| ] | |
| ] | |
| }, | |
| "body_acc": "total_acc - lowpass(total_acc)" | |
| }, | |
| { | |
| "op": "normalise", | |
| "rule": "(x - mean) / std per channel, training-subject constants" | |
| }, | |
| { | |
| "op": "quantise", | |
| "rule": "round(x / input_scale) + input_zp, clipped to int8" | |
| } | |
| ], | |
| "filter_form": "second-order sections, transposed direct form II, causal; state starts at the steady state of the first sample", | |
| "chosen_by_the_zoo": [ | |
| "median length 3", | |
| "gravity filter order 3", | |
| "causal filtering" | |
| ] | |
| }, | |
| "benchmark": "uci-har-v1" | |
| } | |