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The overall best performing feature set was the proposed model-based neural set (Fig. 7), although a significant advantage resulting from the model-based dimensionality reduction was only observed at the shortest ramp duration (the fastest speed). This is likely due to the decomposition being less accurate for faster contractions, in which case a model-based approach could recover more information. At the same time, TD might benefit from more careful DoF-wise channel selection in addition to PCA. At this stage, the computational load required for extracting neural features is much greater compared to the TD features. The implementation and results presented in this study aimed at a rigorous testing of the concept, to prove the feasibility of the neural approach. Future work should explore online controllers and test their clinical validity with the focus on the implications of the observed increase in offline performance.
The main limitation of the study is that we used an offline automatic EMG decomposition method, which is not invariant to the movements of muscles relative to the skin surface since it has been developed for low to medium force isometric contractions and has been shown to be only partly effective for dynamic contractions [36]. We also do recognize that the improvements in the offline control do not necessarily result in the increase of clinical scores [37]. Based on the statistically significant improvement in the offline scores, it is indeed difficult to conclude how beneficial the observed increase in clinical performance will be. However, in this study, we aimed to investigate whether the information gained from EMG decomposition can in principle benefit myoelectric control. Moreover, the presented evaluation is not dependent on the data acquisition method, and can be used with any method for extracting spike trains of motor unit populations, including future online EMG decomposition algorithms of surface or intramuscular EMG [38], as well as spike sorting from other signals such as peripheral nerve recordings [39,40,41]. It should also be noted that an online implementation of the method used here is feasible [42], and it is also possible to implement an MU tracking algorithm [20] that can provide continuous information on the activity of the relevant MUs while at the same time reducing the computational time needed for signal decomposition.
Most of the servers in its network were online and functional when I was testing it out. However, I was disappointed when I was unable to use the Sydney and Japan servers. After deeper research, I realized that over ten locations had offline servers.
Cross-media exposure, measured as the entropy of information sources in a customer journey, drives purchase decisions. The positive effect is particularly pronounced for (1) digital (online) versus traditional (offline) media environments, (2) customers who currently do not own the brand and (3) brands that customers perceive as weak.
Exposures across multiple media should be more informative for customers than receiving information from a single medium. Extensive research also suggests a positive effect of such media synergies on sales (e.g. Jayson et al., 2018; Naik and Raman, 2003; Pauwels et al., 2016; Srinivasan et al., 2016). These studies distinguish within-media synergies, such as between television and print advertising (e.g. Naik and Raman, 2003); cross-media synergies between offline and online channels (e.g. Naik and Peters, 2009); and cross-media synergies between different information sources, such as company-controlled and peer-driven media (e.g. Jayson et al., 2018).
Compared with traditional, offline environments, digital channels typically are associated with greater uncertainty and lower perceptions of the clarity and credibility of paid and earned media (eMarketer, 2017; Nielsen, 2015). The incremental information gains caused by cross-media exposure thus should be more effective in digital (online) compared with traditional (offline) channels. That is, we propose that increases in media entropy are more effective in the online than in the offline channel. Thus,H1. The increase of media entropy, measured as the extent of a customer's cross-media exposure in the customer journey, has a positive effect on customers' brand purchase likelihood.
We seek to provide and test a metric of the effect of media entropy on an individual customer level. Because we need data on individual customer journeys, we employed an experience tracking approach. Instead of collecting data retrospectively, this approach asks respondents to report all their encounters with competing brands directly after the encounter (Baxendale et al., 2015), through their smartphone devices. It thus can collect data on individual brand encounters across media types (i.e. paid and earned) and channels (i.e. offline and online). It also reduces the cognitive burden associated with recalling brand encounters and the memory decay that can arise with retrospective surveys (Danaher and Dagger, 2013). For an overview of this data collection method, see Baxendale et al. (2015) or Lovett and Peres (2018).
We identify whether each media touchpoint represents one of four possible categories: offline paid, offline earned, online paid or online earned. As outlined, we operationalize cross-media exposure as the entropy of paid and earned media types calculated over all previous media touchpoints in the customer journey.
Table 2 contains the main results pertaining to how media exposure across paid and earned media, as measured by media entropy, affects customers' purchase likelihood. In Model 1, we calculate media entropy across media types. In Model 2, we separately calculate the effect of the media entropy metric in offline and online channels, to assess the effect of media entropy in digital (online) and traditional (offline) media environments. The parameter estimates for the media entropy variable are consistent with our theory; media entropy appears to have a positive effect on customers' brand purchase likelihood. Specifically, we find that customers who encounter more diversity in media types are also more likely to purchase the brand (media entropy = 0.367), in support of H1.
We find varying effectiveness of media entropy across offline and online channels. Specifically, the coefficient of media entropy within the online channel is greater than that within the offline channel (media entropy (offline) = 0.089, media entropy (online) = 0.585). Enhancing media entropy in the online channel likely increases purchase likelihood more than enhancing in media entropy in the offline channel, as we predicted in H2.
We specifically assess the effect of media entropy in digital (online) environments and find that media entropy is more effective in driving purchase likelihood in digital (online) than traditional (offline) environments. This effect is due to the information gains caused by cross-media exposure in digital channels, which typically are associated with higher uncertainty and perceived as less clear and credible than traditional media (eMarketer, 2017; Nielsen, 2015). Our findings thus shed new light on the effects of new media on purchase decisions (Hennig-Thurau et al., 2010; Lamberton and Stephen, 2016).
The most efficient classifier algorithm for offline decoding was selected by comparing the classification accuracies obtained with two commonly used feature extraction methods (CSP and STFT) and four different classifiers implemented in the Scikit-learn software package version 0.17.1 [42]. The efficiency was tested with the left-vs-right classification, since it was considered the most difficult classification task. The classifiers included in this comparison were linear discriminant analysis (LDA), support vector machines with a linear (Linear SVM) and radial basis function (RBF SVM) kernel, and Naïve Bayes (NB). LDA yielded the best accuracy among these classifiers, although the difference was not significant (see Section 3.2). Therefore, LDA was selected for comparing the efficacy of the different features.
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Passive sensing of the political sphere involves the use of social media and IoT technologies by politicians or value articulating institutions to influence civic opinion or guide certain behaviours47. For example, social media companies often seek to persuade and influence individuals and groups to take certain actions48 or to understand their civic and political participatory behaviour, both online and offline49. Others have started using IoT technologies to understand the complex evolution of legal systems8. Active sensing of the political sphere includes, for example, games that enable stakeholders and institutional decision-makers to assess the trade-offs associated with different sustainability and resilience policies50. They increasingly feature in climate change communication, participatory research and collaborative learning45 and can often be part of ULL activities.
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