Zachary / Classification of EMG siginal Public

Target: Raspberry Pi RP2040 (Cortex-M0+ 133MHz) Clone this project

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Classification of EMG siginal

About this project

This job is developed by 张权 for his graduation project! It can only be used by person and cannot be used for personal benefit.

Data (180 samples)

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dumbbell5.csv.2v7v0r3e.s7.s1

ID: 102591972

Label: dumbbell

Sample length: 500ms

dumbbell1.csv.2v7v0qb5.s6.s1

ID: 102592006

Label: dumbbell

Sample length: 500ms

dumbbell4.csv.2v7v0qfg.s11.s1

ID: 102591982

Label: dumbbell

Sample length: 500ms

hand_grip1.csv.2v7v0rqd.s2.s1

ID: 102591962

Label: hand_grip

Sample length: 500ms

dumbbell1.csv.2v7v0qb5.s11.s1

ID: 102592010

Label: dumbbell

Sample length: 500ms

idle1.csv.2v7v1129.s6.s1

ID: 102591908

Label: idle

Sample length: 500ms

idle2.csv.2v7v11pm.s24.s1

ID: 102591898

Label: idle

Sample length: 500ms

dumbbell1.csv.2v7v0qb5.s8.s1

ID: 102592011

Label: dumbbell

Sample length: 500ms

Model accuracy, Time series data, Flatten, Classification (Keras) (Unoptimized (float32))

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Validation set

100.0%

Test set

100.0%

On-device performance (Raspberry Pi RP2040 (Cortex-M0+ 133MHz), Quantized (int8))

Latency

1 ms.

Peak RAM usage

1.7K

Flash usage

18.6K

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Dataset summary

Data collected

1m 30s

Sensor

adc @ 100Hz

Labels

dumbbell, hand_grip, idle

Project info

Project ID	107747
Project version	1
License	No license attached
No. of views	6,359
No. of clones	31