  Electromyogram (EMG) pattern recognition can be used to classify hand
gestures and movements for human-machine interface and prosthetics
applications, but it often faces reliability issues resulting from limb
position change. One method to address this is dual-stage classification, in
which the limb position is first determined using additional sensors to select
between multiple position-specific gesture classifiers. While improving
performance, this also increases model complexity and memory footprint, making
a dual-stage classifier difficult to implement in a wearable device with
limited resources. In this paper, we present sensor fusion of accelerometer and
EMG signals using a hyperdimensional computing model to emulate dual-stage
classification in a memory-efficient way. We demonstrate two methods of
encoding accelerometer features to act as keys for retrieval of
position-specific parameters from multiple models stored in superposition.
Through validation on a dataset of 13 gestures in 8 limb positions, we obtain a
classification accuracy of up to 93.34%, an improvement of 17.79% over using a
model trained solely on EMG. We achieve this while only marginally increasing
memory footprint over a single limb position model, requiring $8\times$ less
memory than a traditional dual-stage classification architecture.
