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Significance: The performance of traditional approaches to decoding movement intent from electromyograms (EMGs) and other biological signals commonly degrade over time. Furthermore, conventional algorithms for training neural network-based decoders may not perform well outside the domain of the state transitions observed during training. The work presented in this paper mitigates both these problems, resulting in an approach that has the potential to substantially he quality of live of people with limb loss. Objective: This paper presents and evaluates the performance of four decoding methods for volitional movement intent from intramuscular EMG signals. Methods: The decoders are trained using dataset aggregation (DAgger) algorithm, in which the training data set is augmented during each training iteration based on the decoded estimates from previous iterations. Four competing decoding methods: polynomial Kalman filters (KFs), multilayer perceptron (MLP) networks, convolution neural networks (CNN), and Long-Short Term Memory (LSTM) networks, were developed. The performance of the four decoding methods was evaluated using EMG data sets recorded from two human volunteers with transradial amputation. Short-term analyses, in which the training and cross-validation data came from the same data set, and long-term analyses training and testing were done in different data sets, were performed. Results: Short-term analyses of the decoders demonstrated that CNN and MLP decoders performed significantly better than KF and LSTM decoders, showing an improvement of up to 60% in the normalized mean-square decoding error in cross-validation tests. Long-term analysis indicated that the CNN, MLP and LSTM decoders performed significantly better than KF-based decoder at most analyzed cases of temporal separations (0 to 150 days) between the acquisition of the training and testing data sets. Conclusion: The short-term and long-term performance of MLP and CNN-based decoders trained with DAgger, demonstrated their potential to provide more accurate and naturalistic control of prosthetic hands than alternate approaches. 

This paper presents a framework for shared, human-machine control of a prosthetic arm. The method employs electromyogram and peripheral neural signals to decode motor intent, and incorporates a higher-level goal in the controller to augment human effort. The controller derivation employs Markov Decision Processes. The system is trained using a gradient ascent approach in which the policy is parameterized using a Kalman Filter and the goal is incorporated by adapting the Kalman filter output online. Results of experimental performance analysis of the shared controller when the goal information is imperfect are presented in the paper. These results, obtained from an amputee subject and a subject with intact arms, demonstrate that a system controlled by the human user and the machine together exhibit better performance than systems employing machine-only or human-only control. 

This paper presents a framework for modeling neural decoding using electromyogram (EMG) and electrocorticogram (ECoG) signals to interpret human intent and control prosthetic arms. Specifically, the method of this paper employs Markov Decision Processes (MDP) for neural decoding, parameterizing the policy using an artificial neural network. The system is trained using a modification of the Dataset Aggregation (DAgger) algorithm. The results presented here suggest that the approach of the paper performs better than the state-of-the-art. 

Cancer affects one in three people worldwide. Surgery remains the primary curative option for localized cancers, but good prognoses require complete removal of primary tumors and timely recognition of metastases. To expand surgical capabilities and enhance patient outcomes, we developed a six-channel color/near-infrared image sensor inspired by the mantis shrimp visual system that enabled near-infrared fluorescence image guidance during surgery. The mantis shrimp’s unique eye, which maximizes the number of photons contributing to and the amount of information contained in each glimpse of its surroundings, is recapitulated in our single-chip imaging system that integrates arrays of vertically stacked silicon photodetectors and pixelated spectral filters. To provide information about tumor location unavailable from a single instrument, we tuned three color channels to permit an intuitive perspective of the surgical procedure and three near-infrared channels to permit multifunctional imaging of optical probes highlighting cancerous tissue. In nude athymic mice bearing human prostate tumors, our image sensor enabled simultaneous detection of two tumor-targeted fluorophores, distinguishing diseased from healthy tissue in an estimated 92% of cases. It also permitted extraction of near-infrared structured illumination enabling the mapping of the three-dimensional topography of tumors and surgical sites to within 1.2-mm error. In the operating room, during surgical resection in 18 patients with breast cancer, our image sensor further enabled sentinel lymph node mapping using clinically approved near-infrared fluorophores. The flexibility and performance afforded by this simple and compact architecture highlights the benefits of biologically inspired sensors in image-guided surgery.