Investigating the effects of two fragrances on cabin comfort in an automotive environment.

BACKGROUND: For passengers in private or public transportation systems, comfort is a major interest. Available comfort models are already used to correlate thermal comfort to influencing factors. However, the available models do not other sensory comfort aspect and specific influences as fragrances and ambient light. OBJECTIVE: This publication investigates the impact of fragrances with "warm" and "cold" associated meanings on thermal and overall comfort perception. METHODS: Human subject trials (n = 47) were performed in different temperature-controlled environments following a 3×3 within-subject design considering ambient fragrance ("neutral scent", "peppermint", "orange & cinnamon") and ambient light as variables. RESULTS: Olfactory comfort is shown to have the larger effect on overall comfort perception, comparable in weight to the one of thermal comfort. The impact observed on thermal sensation was in line with the meanings associated the fragrances, whereas it was positive on thermal comfort appreciation regardless of the type of fragrance diffused. CONCLUSIONS: These initial results suggest that olfactory stimulations have the potential to positively impact thermal and overall comfort. The appreciation of the fragrance appears to have a major impact on these interactions and should be deeply considered in future research and features development.

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks.

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.