Shape restrictions on the kernel regression function can be imposed using various approach.
One general method was developed in Du, Parmeter and Racine (2013). The basic idea is to impose the constraints (here monotonicity) through weights in a generalized kernel estimator in the spirit of Hall and Huang (2001). The procedure involves solving a quadratic program with linear inequality constraints.