A random ensemble of random perceptrons is studied and applied in fall detection and categorization, an important and growing problem in Ambient Assisted Living and other fields related to the care of elder and in general of “fragile” people. The classifier ensemble is designed around an ECOC aggregator and compensates for the lack of an accurate training with the number of base learners, which increases accuracy and strengthens the error-correcting capabilities of class codewords. The approach is suitable when some memory is available, but computational power is limited: this is the standard situation in mobile computing, and to an even larger extent in wearable computing. Performances on the two applicative tasks of fall recognition (dichotomic) and categorization (multi-class) are compared with those of support vector machines.