Estimation of the Torques Produced by Human Upper Limb during Eating Activities Using NARX-NN

Figures & data

Figure 1. Isometric view of the 4-DOF robotic system (a) CAD drawing (b) during the system.

Figure 2. Three main events were identified during each eating activity: (a) Origin (b) Event A (c) Event B (d) Event D.

Table 1. Training validation performance of NARX-NNs.

NARX-NN	Training activity	Mean Squared Error (MSE)	Validation regression (R)
NN1	Soup	2.0875e-06	0.999
NN2	Cereal	7.070e-05	1
NN3	Soup	1.059e-07	1
NN4	Rice	1.035e-07	0.999

Figure 3. (a) Motion trajectories, (b) Elbow flexion/extension $\left(T_1\right)$, (c) forearm pronation/supination $\left(T_2\right)$, (d) wrist adduction/abduction $\left(T_3\right)$, and (e) wrist flexion/extension $\left(T_4\right)$ comparison plots, during cereal eating activity of Subject 1.

Table 2. Average RMSE of torque predictions by NARX-NN for all subjects.

	AVG	T1 (Nm) RMSE	T2 (Nm) RMSE	T3 (Nm) RMSE	T4 (Nm) RMSE
CEREAL		0.0533	0.0582	0.0986	0.0362
RICE		0.0867	0.0681	0.1078	0.0600
NOODLE		0.1831	0.0672	0.0472	0.0476
SOUP		0.0583	0.0684	0.1010	0.0938
VEG		0.1037	0.0808	0.0673	0.0491

Figure 4. HUL and robotic system setup showing the placement of MTWs on the forearm & hand.

Figure 5. Flowchart showing input and target training datasets of 4 individual NARX models used for torque estimation during various eating activities.

Figure 6. Validation performance of $T_1$ NARX model.

Figure 7. Regression values of $T_1$ NARX model.

Figure 8. Error autocorrelation function of $u\left(t\right)$ NARX model.

Figure 9. Input-error cross-correlation function of $T_1$ NARX model.

Figure 10. Time series response of $T_1$ NARX model.