Unpacking the hazards: An analytic study of injury patterns and risk factors in urban instant delivery.

The rapid growth of urban instant delivery, facilitated by digital platforms and characterized by on-demand, short-term, task-based labor, has raised concerns about safety, particularly with the increasing frequency of instant delivery crashes (IDCs). This study addresses knowledge gaps in understanding injury patterns and risk factors associated with IDCs. Utilizing data extracted from judicial verdicts on IDC disputes in China, encompassing demographic, contextual, crash, and injury information, the research employs ordered logit regression to identify significant factors affecting injury patterns, the number of injuries per person (IPP), and injury severity. Overall, traffic injuries related to instant delivery services have gradually improved since 2020, as evidenced by the severity of individual accidents, the number of injuries, and the economic losses. Analysis of 648 injuries among 448 non-fatal victims reveals a prevalence of lower extremity injuries, followed by external, upper extremity, and head injuries. While the majority of victims suffered a single injury, approximately 22 % experienced major injuries. Female delivery riders exhibited higher injury ratios across various body regions. Rider risk behavior, type of delivery vehicles, and the mode of transport of non-delivery travelers emerged as significant influencers of injury patterns. Notably, functional and physical intersection areas exhibited the highest injury ratios among facility types. Contrary to conventional wisdom, older riders and travelers aged above 50 were associated with higher injury severity, challenging the perception of young age as the primary risk factor. The prominence of lower extremity injuries underscores the necessity for heightened protective measures for delivery riders. Major injuries among victims emphasize potential long-term consequences and associated costs. The significance of gender, age, and risk behavior as determining factors highlights the need for targeted safety interventions. These findings offer crucial insights for stakeholders, guiding the formulation of precise safety measures and informed policy initiatives within the dynamic landscape of instant delivery safety.

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI.

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.

Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach.

With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate. In this work, we validated the feasibility of EEG-based authentication in a real-world, out-of-laboratory setting using a commercial dry-electrode EEG headset and chronic recordings on a population of 15 healthy people. We used an LSTM-based network with bootstrap aggregating (bagging) to decode our recordings in response to a multitask scheme consisting of performed and imagined motor tasks, and showed that it improved the performance of the standard LSTM approach. We achieved an authentication accuracy, false acceptance rate (FAR), and false rejection rate (FRR) of 92.6%, 2.5%, and 5.0% for the performed motor task; 92.5%, 2.6%, and 4.9% for the imagined motor task; and 93.0%, 1.9%, and 5.1% for the combined tasks, respectively. We recommend the proposed method for time- and data-limited scenarios.