In this thesis, we establish that the IPUS approach for the Integrated Processing and Understanding of Signals can be used to address the signal representation challenges in an application involving complex biosignals from a highly unscripted and unconstrained real-world environment. Specifically, we have designed an IPUS-based system to detect movement disorders on a per-second basis in Parkinson's disease (PD) patients wearing small numbers of surface electromyographic (sEMG) and triaxial accelerometric (ACC) sensors. It is also designed to operate without the need for any patient-specific training. When evaluated on a database of 44 hours of sensor signals, our system is found to yield detection error rates significantly below 10% for each of the movement disorders. Such low error rates have previously been reported only for systems that severely constrain the physical activities performed by the PD patients.

In developing the signal processing for the IPUS system, we first established that various short-term autocorrelation-based features of the sEMG and ACC signals are an appropriate basis for the classification of tremor, dyskinesia, and freezing-of-gait. Next, as a key innovation within the IPUS approach, we designed machine learning algorithms (dynamic neural networks, dynamic support vector machines, and hidden Markov model algorithms) that on the basis of those features produce numerical ratings between −1 and +1 for every one second of the input data. We then designed rule-based signal understanding algorithms that convert the sequences of ratings into inferences about the presence or absence of the disorders and that detect various conditions that are then used as triggers to prompt changes in the signal processing. This triggering mechanism completes the IPUS loop for changing the signal processing on the fly, on a per-need basis.

In conclusion, the research results presented in this thesis illustrate the power of combining machine learning algorithms and IPUS-based adaptation of signal processing in order to successfully address a complex biosignal application. Our results also constitute a proof of principle and a potential blueprint for the development of solutions to other complex real-world biosignal problems.