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This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

This paper describes the application of, enhancements to, and use of surface fire spread models in predicting and mitigating fire risk in the Wildland–Urban Interface (WUI). Research and fire management strategies undertaken in the East Bay Hill region (containing the 1991 Tunnel Fire) of the San Francisco Bay area over the past decade are reported. We ascertain that surface fire spread modeling has impacted policy and decision making, resulting in a regional strategic plan where large landowners and public agencies are able to implement fire mitigation practices. Although these practices involve extensive fuel management within a buffer zone between the wildland and residential properties, the residential property owners are still at risk, as no strategy within neighborhoods can be accurately mapped using the current scale of the data and models. WUI fires are eventually extinguished by fire fighters on the ground, up close, and at the backyard scale. We argue that large-scale (backyard scale) mapping and modeling of surface fire spread is necessary to engage the individual homeowner in a fuels management strategy. We describe our ongoing research and strategies, and suggest goals for future research and development in the area of large-scale WUI fire modeling and management.

Peripheral arterial disease (PAD) is an occlusive atherosclerotic disease that affects blood vessels and reduces blood flow in the lower limbs. It is estimated that around 200 million people worldwide suffered from it, with a significant number of older people affected. Walking is one of the first-line therapeutic measures for intermittent claudication (IC) in patients with PAD. Supervised Exercise Therapy (SET) programs effectively increase walking distances, however, remain an underutilized tool because they are not readily available in most clinical centres, are extremely expensive, and patient participation is low mainly due to socioeconomic constraints. Home-based Exercise Therapy (HBET) programs are an effective and low-cost alternative to improve both the functional capacity and quality of life (QoL) of patients with IC, as they are performed in the patient’s area of residence and not in the hospital. The WalkingPad program conciliated a smartphone app – the WalkingPad app – with behaviour change intervention to increase walking distances and decrease walking impairment as well to improve QoL at 6 months.

The central focus of this chapter is the mutually constitutive relationship between time and group interaction. Groups shape individuals' experiences of time and individuals' experiences of time enable and constrain their group interactions. The chapter begins with a brief history of time in groups to situate early concerns which still shape many contemporary investigations, and then examines several theoretical perspectives as well as midrange frameworks and constructs which inform research on time and group communication. The chapter concludes with a summary and directions for future research in the area.