Background: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). New method: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. Results: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 μs for each sample in the data window analyzed). Comparison with existing method: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. Conclusions: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.
Keywords: Neuroprosthetics | Neural signals | Spike sorting | Pattern recognition | EMG signals

Innovation holds the guarantee of opening new doors for enhancing the nature of our lives. Likewise, a worldwide data and correspondence system superstructure is developing as the Internet transforms into the Internet of Things. The Internet of Things will address an impressive number of current difficulties in all application spaces, and will add to eco nomic development. The most critical application spaces of the pervasiveness of Internet of Things-based innovations are exhibited, including the case of the shrewd urban com munities presently being studied, which will improve the standard of ordinary lives. As a prologue to this issue of general economic systems research, which is themed around mechanical biology, the paper intends to discuss the foundation of modern nature, while highlighting the role of, and commitments from, financial matters from the point of view of information security. The performance is verified by experiment.
Keywords: Internet of Things | Biological ambient intelligence | Wireless sensor and actor networks | Decision tree | Artificial neural controller | Organization under surveillance and control