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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.

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.