pace-cnn / config.json
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{
"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"
}