TABLE XV.
Summary of Activity and Gesture Detectors for Microcontrollers
Method | Sensor(s) | Task | Accuracy | Flash |
---|---|---|---|---|
GesturePod ProtoNN [212] | MPU6050 inside white cane | Detect 5 cane gestures | 92% | 6 kB |
Auritus FastRNN [2] | eSense earable | Detect 9 macro activities | 98% | 6 kB |
Bian et al. 1D-CNN [213] | Wrist-worn capacitive array | Detect 7 hand gestures | 96% | 30 kB |
T’Jonck et al. CNN [214] | BMA400 inside mattress | Detect 5 bed activities | 89% | < 1 MB |
Zhou et al. HDC + SVM [215] | MPU-6050 and EMG pad (wrist-worn) | Detect 13 hand gestures across 8 limb positions | 93% | 135 kB |
Elsts et al. CNN [216] | Colibri Wireless IMU | Detect 18 macro activities | 73% (F1 score) | 20 kB |
Coelho et al. DT [217] | Chest, waist and ankle-mounted IMU | Detect 12 macro activities | 97% | 22 kB |
FastGRNN (LSQ) [57] | IMU on torso and limbs | Detect 6 macro activities and 19 sports activities | 96% 84% |
3 kB 3.25 kB |