EMG data processing and muscle activity recognition has become the most popularmethod for upper limb prosthetics. The high sensitivity of EMG sensors with respect to externaldisturbances and other factors prevent from accurate muscle activity recognition. The aim of thepaper is to investigate robustness of window recognition method with respect to muscle fatigueand perspiration of the forearm skin. The current experiment was carried out using Arduinonano microcontroller connected to EMG sensors. The subject under study is a healthy man of 26years old with an average build. The subject was asked to do physical exercises, thereby loadingthe muscles of the fingers of the hand to achieve partial or complete fatigue and perspiration.During the whole process, EMG sensors have installed on the subject and transmitted the signalto the computer using Arduino. All signal processing is done directly on the computer with apre-recorded signal. Experimental results have been shown that with the appearance of externalfactors during prosthesis operation recognition accuracy may degrade to unsatisfactory. Falsepositives occur with perspiration of skin surface and complete muscle fatigue. An algorithmfor automatic self-correction of the boundaries of motion detection zones has been introduced.Instead of identification of causes that leads to performance degradation, we use correctionscheduling started by timer. Experimental results have shown that proposed automatic adaptivecorrection is effective. Despite higher recognition delay, proposed auto-tuning method providessatisfactory muscle activity identification and feature extraction in real-time.