Develop Habit-forming Products Based on the Axiomatic Design Theory

AbstractIt is every manufacturer's desire to drive its target customers to form a long-term habit of regularly using its product. Previous studies indicate that the habit of using a certain product can indeed by formed in a systemic manner, once the right sequence is followed. Against such a background, an existing habit-forming product model, namely the Hook Model, is reviewed with respect to its key components of trigger, action, reward, and investment. Essences of the Hook Model, together with its missing pieces, are reformulated, repositioned, and resynthesized based on the Axiomatic Design Theory. It results in an adapted Axiomatic Design process, which is intended to develop the habit-forming products. The step-by-step design process is explained, and an illustrate example is presented

Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018

An evaluation study of miniature dielectric crossed compound parabolic concentrator (dCCPC) panel as skylights in building energy simulation

The potential of miniature dielectric crossed compound parabolic concentrator (dCCPC) panel as skylights for daylighting control has drawn a considerable research attention in the recent years, owing to its feature of variable transmittance according to the sun position, but the viability of using it as skylights in buildings has not been explored yet comprehensively. This paper aims to study the feasibility of utilizing miniature dCCPC panel as skylight in different locations under various climates in terms of energy saving potential besides its daylighting control function. The transmittance of dCCPC panel varies at every moment according to the sky condition and sun position. Due to this specific property, this study novelly implemented a polynomial formula of the dCCPC transmittance in the Grasshopper platform, from which EnergyPlus weather data can be called to calculate the hourly transmittance data of dCCPC skylight panel throughout the whole year. An hourly schedule of transmittance is generated according to the hourly sky condition determined by the daylight simulation through Radiance and Daysim, and is then input to EnergyPlus simulation to predict the energy consumption of a building with dCCPC skylight. Fourteen locations around the world are therefore compared to find the most appropriate place for using miniature dCCPC panel as skylights. The energy saving in cooling, heating and lighting with use of dCCPC skylight panel are investigated and compared with low-E and normal double glazing. The results show that the dCCPC skylight panel can reduce cooling load by mitigating solar heat gain effectively although its performance is affected by several criteria such as sky conditions and local climates. It is generally more suitable for the locations with longer hot seasons, e.g., Log Angeles, Miami, Bangkok and Manila, in which dCCPC could provide up to 13% reduction in annual energy consumption of building. For the locations having temperate and continental climates like Beijing, Rome, Istanbul and Hong Kong, a small annual energy saving from 1% to 5% could be obtained by using dCCPC skylight panel

Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos

Remote photoplethysmography (rPPG) based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, \etc, in a non-contact manner. Existing mainstream CNN-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native Transformer-based framework called Dual-path TokenLearner (Dual-TL) is proposed in this paper, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed Dual-TL uses a Spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a Temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The Dual-TL achieves state-of-the-art performances in both intra- and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement. The source code is available at \href{https://github.com/VUT-HFUT/Dual-TL}{https://github.com/VUT-HFUT/Dual-TL

The therapeutic potential of GABA in neuron-glia interactions of cancer-induced bone pain

Abstract: The development of effective therapeutics for cancer-induced bone pain (CIBP) remains a tremendous challenge owing to its unclear mechanisms. Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the central nervous system. Emerging studies have shown that disinhibition in the spinal cord dorsal horn may account for the development of chronic pain. However, the role of GABA in the development of CIBP remains elusive. In addition, accumulating evidence has shown that neuroglial cells in the peripheral nervous system, especially astrocytes and microglial cells, play an important role in the maintenance of CIBP. In this study, we investigated the expression of GABA and Gamma-aminobutyric acid transporter-1 (GAT-1), a transporter of GABA. Our results demonstrate that GABA was decreased in CIBP rats as expected. However, the expression of glutamic acid decarboxylase (GAD) 65 was up-regulated on day 21 after surgery, while the expression of glutamic acid decarboxylase (GAD) 67 remained unchanged after surgery. We also found that the expression of GAT-1 was up-regulated mainly in the astrocytes of the spinal cord. Moreover, we evaluated the analgesic effect of exogenous GABA and the GAT-1 inhibitor. Intrathecal administration of exogenous GABA and NO-711(a GAT-1 selective inhibitor) significantly reversed CIBP-induced mechanical allodynia in a dose-dependent manner. These results firstly show that neuron-glia interactions, especially on the GABAnergic pathway, contribute to the development of CIBP. In conclusion, exogenous GABA and GAT-1 inhibitor might be alternative therapeutic strategies for the treatment of CIBP. Keywords: Cancer-induced bone pain; Gamma-Aminobutyric acid; Glutamic acid decarboxylases; GABA transporters; NO-711; Astrocyt