Context-informed incremental learning improves both the performance and resilience of myoelectric control.

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition.

Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to 'confounding factors' (i.e. factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1)limb position variability, (2)cross-day use, and a newly identified confound faced by discrete systems (3)gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

Comparison of Feedback Approaches to Improve Training in Partial Weight-Bearing.

Assistive devices, including canes or crutches, are used in partial weight-bearing (PWB) to offload weight from limbs weakened by disease or injury, promote recovery, and prevent reinjury. While weight must be offloaded accurately to target loads prescribed by healthcare providers for maximum benefit, current training methods result in poor adherence. It is, however, currently unknown how best to provide feedback during training so that users can build an accurate internal model for PWB. In this work, we investigate seven feedback schemes using an instrumented cane, which vary the modality, timing, and the level of detail provided. We find that auditory schemes and a retrospective visual scheme outperform current clinical practices for PWB training. These findings provide results that can be applied directly to improve current clinical practices and provide valuable new insight into the design of feedback for training internal models in force-based motor control tasks. Clinically, this work presents a simple modification to clinical PWB training practices that can improve compliance by up to 75%, positively influencing rehabilitation outcomes and reducing the risk of complications.

Wearable EMG-Based Gesture Recognition Systems During Activities of Daily Living: An Exploratory Study.

Recent advancements in wearable technologies have increased the potential for practical gesture recognition systems using electromyogram (EMG) signals. However, despite the high classification accuracies reported in many studies (> 90%), there is a gap between academic results and industrial success. This is in part because state-of-the-art EMG-based gesture recognition systems are commonly evaluated in highly-controlled laboratory environments, where users are assumed to be resting and performing one of a closed set of target gestures. In real world conditions, however, a variety of non-target gestures are performed during activities of daily living (ADLs), resulting in many false positive activations. In this study, the effect of ADLs on the performance of EMG-based gesture recognition using a wearable EMG device was investigated. EMG data for 14 hand and finger gestures, as well as continuous activity during uncontrolled ADLs (>10 hours in total) were collected and analyzed. Results showed that (1) the cluster separability of 14 different gestures during ADLs was 171 times worse than during rest; (2) the probability distributions of EMG features extracted from different ADLs were significantly different (p <; 0.05). (3) of the 14 target gestures, a right angle gesture (extension of the thumb and index finger) was least often inadvertently activated during ADLs. These results suggest that ADLs and other non-trained gestures must be taken into consideration when designing EMG-based gesture recognition systems.

Evaluation of Myoelectric Control Learning Using Multi-Session Game-Based Training.

While training is critical for ensuring initial success as well as continued adoption of a myoelectric powered prosthesis, relatively little is known about the amount of training that is necessary. In previous studies, participants have completed only a small number of sessions, leaving doubt about whether the findings necessarily generalize to a longer-term clinical training program. Furthermore, a heavy emphasis has been placed on a functional prosthesis use when assessing the effectiveness of myoelectric training. Although well-motivated, this all-inclusive approach may obscure more subtle improvements made in underlying muscle control that could lead to tangible benefits. In this paper, a deeper exploration of the effects of myoelectric training was performed by following the progress of 30 participants as they completed a series of ten 30-min training sessions over multiple days. The progress was assessed using a newly developed set of metrics that was specifically designed to quantify the aspects of muscle control that are foundational to the strong myoelectric prosthesis use. It was determined that, while myoelectric training can lead to improvements in muscle control, these improvements may take longer than previously considered, even occurring after improvements in the training game itself. These results suggest the need to reconsider how and when transfer from training activities is assessed.

Superior therapeutic efficacy of nab-paclitaxel over cremophor-based paclitaxel in locally advanced and metastatic models of human pancreatic cancer.

BACKGROUND: Albumin-bound paclitaxel (nab-paclitaxel, nab-PTX) plus gemcitabine (GEM) combination has demonstrated efficient antitumour activity and statistically significant overall survival of patients with metastatic pancreatic ductal adenocarcinoma (PDAC) compared with GEM monotherapy. This regimen is currently approved as a standard of care treatment option for patients with metastatic PDAC. It is unclear whether cremophor-based PTX combined with GEM provide a similar level of therapeutic efficacy in PDAC. METHODS: We comprehensively explored the antitumour efficacy, effect on metastatic dissemination, tumour stroma and survival advantage following GEM, PTX and nab-PTX as monotherapy or in combination with GEM in a locally advanced, and a highly metastatic orthotopic model of human PDAC. RESULTS: Nab-PTX treatment resulted in significantly higher paclitaxel tumour plasma ratio (1.98-fold), robust stromal depletion, antitumour efficacy (3.79-fold) and survival benefit compared with PTX treatment. PTX plus GEM treatment showed no survival gain over GEM monotherapy. However, nab-PTX in combination with GEM decreased primary tumour burden, metastatic dissemination and significantly increased median survival of animals compared with either agents alone. These therapeutic effects were accompanied by depletion of dense fibrotic tumour stroma and decreased proliferation of carcinoma cells. Notably, nab-PTX monotherapy was equivalent to nab-PTX plus GEM in providing survival advantage to mice in a highly aggressive metastatic PDAC model, indicating that nab-PTX could potentially stop the progression of late-stage pancreatic cancer. CONCLUSIONS: Our data confirmed that therapeutic efficacy of PTX and nab-PTX vary widely, and the contention that these agents elicit similar antitumour response was not supported. The addition of PTX to GEM showed no survival advantage, concluding that a clinical combination of PTX and GEM may unlikely to provide significant survival advantage over GEM monotherapy and may not be a viable alternative to the current standard-of-care nab-PTX plus GEM regimen for the treatment of PDAC patients.

Personal Visualization and Personal Visual Analytics.

Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves better and make positive changes in our lives. Visualization (Vis) and visual analytics (VA) offer substantial opportunities to help individuals gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for discussing personal visualization and personal visual analytics. By identifying and exploring clusters in the design space, we discuss challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines. Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will support people to better understand data relevant to their personal lives, interests, and needs.