Calculating the social impact of home automation for people with disability: A social return on investment study.

INTRODUCTION: Home automation can deliver important outcomes for people with disabilities, including enhanced independence. Despite the millions of dollars spent on home automation in Australia and other developed nations, to date, there has been no economic evaluation of this type of assistive technology. METHOD: A social return on investment analysis of home automation study was undertaken. Primary data were collected using qualitative interviews with home automation consumers and other key stakeholders, including occupational therapists, a spinal rehabilitation physician, peer support advocate, and managers and technical personnel from home automation providers (n = 17). The analysis was supported by (1) secondary data from a scoping review on outcomes from home automation and (2) additional literature searches to identify suitable financial proxies and to make estimates of the proportion of home automation users expected to experience each outcome. A scenario approach was used with three home automation scenarios developed with increasing complexity and costs to calculate the social return on investment. RESULTS: Eight outcomes from the use of home automation were identified, including reduced reliance on carers and family members, increased independence, and improved energy and comfort. The social return on investment ranged from $38.80 (low cost) to $15.10 (high cost) for every $1 invested across a 10-year benefit period, with the financial proxy for reduced care attendant hours contributing the most to the social return ratio. Even the highest cost scenario was repaid in social value within the first year of the benefit period. CONCLUSION: This study suggests that home automation represents a sound investment and has a significant impact on the overall quality of life of people with disabilities. Focusing on the financial savings in care attendant hours alone should be compelling evidence for funders to recognise home automation's value and continue to fund this assistive technology. CONSUMER AND COMMUNITY INVOLVEMENT: A consumer representative was a member of the project steering group, which supported the research team at all stages of the project. PLAIN LANGUAGE SUMMARY: When people get injured, their disability can stop them doing things around the home that they used to be able to do. Technology can help people with disabilities do things like open and close doors and turn off taps by pressing a button, so they do not have to wait for someone to help them. This technology can be expensive, but no one has looked at if it is worth the money. We spoke to some people with disabilities who used this type of technology, and they told us their lives were better now they used this technology. For example, they told us they were able to do things for themselves, they did not need carers as much, and they had better mental health. We spoke to businesses about the costs of different types of technology that can be used in the home. We then put a dollar value on the ways people with disabilities told us their lives were better. For example, for better mental health, we worked out how much it would cost to see a psychologist for 1 year. We found that the dollar value of the ways in which people with disabilities' lives were improved was at least 15 times more than the costs of the technology. This study therefore shows that this technology is worth the money and improves the lives of people with disabilities following serious injury.

Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol.

Technological innovation has revolutionized healthcare, particularly in neurological rehabilitation, where it has been used to address chronic conditions. Smart home and building automation (SH&BA) technologies offer promising solutions for managing chronic disabilities associated with such conditions. This single group, pre-post longitudinal pilot study, part of the H2020 HosmartAI project, aims to explore the integration of smart home technologies into neurorehabilitation. Eighty subjects will be enrolled from IRCCS San Camillo Hospital (Venice, Italy) and will receive rehabilitation treatment through virtual reality (VR) and robotics devices for 15 h per day, 5 days a week for 3 weeks in the HosmartAI Room (HR), equipped with SH&BA devices measuring the environment. The study seeks to optimize patient outcomes and refine rehabilitation practices. Findings will be disseminated through peer-reviewed publications and scientific meetings, contributing to advancements in neurological rehabilitation and guiding future research.

The Effects of Home Automation on Personal and Social Autonomies in Spinal Cord Injury Patients: A Pilot Study.

Background: Spinal cord injury (SCI) is a severe and progressive neurological condition caused by trauma to the nervous system, resulting in lifelong disability and severe comorbidities. This condition imposes serious limitations on everyday life, interfering with patients' social lives and compromising their quality of life, psychological well-being, and daily living activities. Rehabilitation is essential to helping SCI patients gain more independence in their daily routines. Home automation (HA) systems provide personalized support to users, allowing them to manage various aspects of their living environment, promoting independence and well-being. This study aims to demonstrate the efficacy of an HA system in enhancing personal and social autonomies in SCI patients, resulting in improved cognitive function and reduced anxiety-depressive symptoms compared to traditional training. Methods: We enrolled 50 SCI patients undergoing neurorehabilitation at IRCCS Centro Neurolesi (Messina, Italy). These patients were randomly assigned to one of two groups: a control group (CG) and an experimental group (EG). The CG received traditional training, while the EG underwent HA training. We evaluated the patients before (T0) and after (T1) rehabilitation using various scales, including the Montreal Cognitive Assessment (MoCA), the Beck Depression Inventory (BDI), the Hamilton Rating Scale for Anxiety (HRS-A), the 12-Item Short-Form Survey (SF-12), the Functional Independence Measure (FIM), Activities of Daily Living (ADL), Instrumental Activities of Daily Living Scale (IADL), and the EQ-5D-5L. Results: The effect of the experimental treatment showed an improvement in all patients test scores in the EG, especially regarding cognitive functions, mood disorders, activities of daily living, and quality of life. Conclusion: Our findings suggest that HA may be effective in improving daily autonomy and, in turn, alleviating mood disorders and enhancing psychological well-being.

Development of a Framework for the Communication System Based on KNX for an Interactive Space for UX Evaluation.

Domotics (Home Automation) aims to improve the quality of life of people by integrating intelligent systems within inhabitable spaces. While traditionally associated with smart home systems, these technologies have potential for User Experience (UX) research. By emulating environments to test products and services, and integrating non-invasive user monitoring tools for emotion recognition, an objective UX evaluation can be performed. To achieve this objective, a testing booth was built and instrumented with devices based on KNX, an international standard for home automation, to conduct experiments and ensure replicability. A framework was designed based on Python to synchronize KNX systems with emotion recognition tools; the synchronization of these data allows finding patterns during the interaction process. To evaluate this framework, an experiment was conducted in a simulated laundry room within the testing booth to analyze the emotional responses of participants while interacting with prototypes of new detergent bottles. Emotional responses were contrasted with traditional questionnaires to determine the viability of using non-invasive methods. Using emulated environments alongside non-invasive monitoring tools allowed an immersive experience for participants. These results indicated that the testing booth can be implemented for a robust UX evaluation methodology.

The experience of using home automation by individuals with disability.

Purpose: People with disability often require long-term support within the home. Advances in technology have made home automation more readily available to support people living with disability. However, few studies describe the perspectives of people using home automation. The aim of this study was to explore the experience of individuals living with long term serious disability using home automation.Materials and methods: A phenomenological approach was adopted. In-depth, semi-structured interviews were conducted. Data were analysed using an inductive approach to identify themes.Results: Two overarching categories of themes were identified: 'benefits' and 'challenges'. Benefits captured the outcomes experienced by people living with disability using home automation and the impact upon their lives. Participants described several challenges with using home automation such as self-advocating to receive home automation, long waiting periods in the assessment and installation process, frustrations when home automation did not work, and the challenges experienced from being without home automation.Conclusion: This research identified the benefits and challenges of home automation experienced by people with long term serious disability. The findings can be used to understand the importance of home automation and the impact it has upon the lives of people living with disability.

It is recognised that home automation can have a positive impact upon the lives of people living with disability.Funding for home automation is a complicated process with long waiting times. This process needs to be readdressed in order for people to receive home automation in a timely manner to prevent negative experiences.Home automation within the community could support people living with disability to access the community more.It is important to develop facilities and communities that are accessible and inclusive for people with disabilities.

A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes.

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents' consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.

Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network.

Gestures have been used for nonverbal communication for a long time, but human-computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected through the images, and the skeleton is computed through mathematical computations. From the skeleton, the features are extracted; these features include joint color cloud, neural gas, and directional active model. After that, the features are optimized, and a selective feature set is passed through the classifier recurrent neural network (RNN) to obtain the classification results with higher accuracy. The proposed model is experimentally assessed and trained over three datasets: HaGRI, Egogesture, and Jester. The experimental results for the three datasets provided improved results based on classification, and the proposed system achieved an accuracy of 92.57% over HaGRI, 91.86% over Egogesture, and 91.57% over the Jester dataset, respectively. Also, to check the model liability, the proposed method was tested on the WLASL dataset, attaining 90.43% accuracy. This paper also includes a comparison with other-state-of-the art methods to compare our model with the standard methods of recognition. Our model presented a higher accuracy rate with a markerless approach to save money and time for classifying the gestures for better interaction.

A Smart Home Digital Twin to Support the Recognition of Activities of Daily Living.

One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data.

A Secure and Smart Home Automation System with Speech Recognition and Power Measurement Capabilities.

The advancement in the internet of things (IoT) technologies has made it possible to control and monitor electronic devices at home with just the touch of a button. This has made people lead much more comfortable lifestyles. Elderly people and those with disabilities have especially benefited from voice-assisted home automation systems that allow them to control their devices with simple voice commands. However, the widespread use of cloud-based services in these systems, such as those offered by Google and Amazon, has made them vulnerable to cyber-attacks. To ensure the proper functioning of these systems, a stable internet connection and a secure environment free from cyber-attacks are required. However, the quality of the internet is often low in developing countries, which makes it difficult to access the services these systems offer. Additionally, the lack of localization in voice assistants prevents people from using voice-assisted home automation systems in these countries. To address these challenges, this research proposes an offline home automation system. Since the internet and cloud services are not required for an offline system, it can perform its essential functions, while ensuring protection against cyber-attacks and can provide quick responses. It offers additional features, such as power usage tracking and the optimization of linked devices.

IoT based smart home automation using blockchain and deep learning models.

For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as "ON" and "OFF" based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis' implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world.

Design and Implementation of a Framework for Smart Home Automation Based on Cellular IoT, MQTT, and Serverless Functions.

Smart objects and home automation tools are becoming increasingly popular, and the number of smart devices that each dedicated application has to manage is increasing accordingly. The emergence of technologies such as serverless computing and dedicated machine-to-machine communication protocols represents a valuable opportunity to facilitate management of smart objects and replicability of new solutions. The aim of this paper is to propose a framework for home automation applications that can be applied to control and monitor any appliance or object in a smart home environment. The proposed framework makes use of a dedicated messages-exchange protocol based on MQTT and cloud-deployed serverless functions. Furthermore, a vocal command interface is implemented to let users control the smart object with vocal interactions, greatly increasing the accessibility and intuitiveness of the proposed solution. A smart object, namely a smart kitchen fan extractor system, was developed, prototyped, and tested to illustrate the viability of the proposed solution. The smart object is equipped with a narrowband IoT (NB-IoT) module to send and receive commands to and from the cloud. In order to evaluate the performance of the proposed solution, the suitability of NB-IoT for the transmission of MQTT messages was evaluated. The results show how NB-IoT has an acceptable latency performance despite some minimal packet loss.

A scoping review to explore the health, social and economic outcomes of home automation for people with disability.

PURPOSE: People with disability often require long-term care. Long-term care is changing with the availability and advances in cost and function of technologies, such as home automation. Home automation has the potential to reduce paid carer hours and can potentially offer many benefits to people with a disability. The aim of this scoping review is to identify the health, social and economic outcomes experienced by people living with a disability who use home automation. MATERIALS AND METHODS: Two electronic databases were searched by title and abstract to identify international literature that describes home automation experiences from the perspectives of people with disability. A thematic approach was taken to synthesise the data to identify the key outcomes from home automation. RESULTS: The review identified 11 studies reporting home automation outcomes for people living with a disability. Seven outcomes were associated with home automation: independence, autonomy, participation in daily activities, social and community connectedness, safety, mental health, and paid care and informal care. CONCLUSION: Advances in technology and changes in funding to support people living with a disability have made access to home automation more readily available. Overall, the study findings showed that there is a range of potential benefits of home automation experienced by individuals living with a disability.Implications for RehabilitationA wide range of outcomes have been evaluated following the installation of home automation systems for people with disability.Key outcomes evaluated to date include independence, autonomy, participation, safety, mental health, and reduced need for paid carers.Outcomes of home automation appear to be connected; for example, improved participation may lead to improved mental health.

Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review.

The management of chronic diseases requires personalized healthcare that allows older adults to manage their diseases at home. This systematic review aimed to describe the smart home technologies used in the management of chronic diseases in older people. A systematic literature review was conducted on four databases and was reported following the PRISMA statement. Nineteen articles were included. The intervention technologies were classified into three groups: smart home, characterized by environmental sensors detecting motion, contact, light, temperature, and humidity; external memory aids, characterized by a partnership between mobile apps and smart home-based activity learning; and hybrid technology, with the integration of multiple technologies, such as devices installed at patients' homes and telemedicine. The health outcomes evaluated are vital signs, medication management, ADL-IADL, mobility, falls, and quality of life. Smart homes show great potential in the management of chronic diseases by favouring the control of exacerbations and increasing patients' safety by providing support in disease management, including support for cognitively impaired older people. The use of smart homes in the community could bring numerous benefits in terms of continuity of care, allowing the constant monitoring of older people by local and hospital health services.

Home Automation for Adults With Disability Following an Injury: Protocol for a Social Return on Investment Study.

BACKGROUND: People with disability following a serious injury require long-term care. The most common injuries resulting in long-term disability are spinal cord and acquired brain injuries. While the long-term effects are difficult to predict and will vary between individuals, the costs of care and recovery span well beyond the initial treatment phase and include long-term care. Long-term care is changing with the availability and advances in cost and function of technologies, such as home automation. "Home automation" refers to technology that automates or remotely controls household functions. Home automation costs vastly differ, but home automation has the potential to positively impact the lives of people with disabilities. However, there is a dearth of evidence relating to the impact of home automation for people with a disability and few rigorous evaluations about the costs and return on investment. OBJECTIVE: The purpose of this study is to describe the impact of home automation for people with long-term disability following a serious injury (such as a motor vehicle accident) using case studies, and by conducting an evaluation of the costs and outcomes for individuals, families, and the wider community using a Social Return on Investment (SROI) approach. METHODS: SROI is a form of economic evaluation that develops a theory of change to examine the relationship among inputs, outputs, and outcomes and, in recent years, has gained popularity internationally, including in Australia. SROI has six phases: (1) identify scope and stakeholders, (2) map outcomes, (3) evidence outcomes and give them value, (4) establish impact, (5) calculate the SROI, and (6) report findings. Individuals with a disability who use home automation and key stakeholders will be interviewed. Stakeholders will be individuals involved in home automation for people with disabilities, such as allied health professionals, medical practitioners, equipment suppliers, engineers, and maintenance professionals. Users of home automation will be people who have a disability following a serious injury, have the capacity to provide consent, and have 1 or more elements of home automation. The impact of home automation will be established with financial proxies and appropriate discounts applied to avoid overestimating the social return. The SROI ratio will be calculated, and findings will be reported. RESULTS: The project was funded in November 2021 by the Lifetime Support Authority. Recruitment is underway, and data collection is expected to be completed by October 2022. The final results of the study will be published in March 2023. CONCLUSIONS: To our knowledge, this study represents the first study in Australia and internationally to employ SROI to estimate the social, personal, and community outcomes of home automation for people with a disability following a serious injury. This research will provide valuable information for funders, consumers, researchers, and the public to guide and inform future decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42493.

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System.

Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households' electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.

Smart home automation technology to support caring of individuals with Alzheimer's disease and related dementia: an early intervention framework.

PURPOSE: Home automation technology comprising of Internet of Things and Smart gadgets is a rapidly growing industry that is projected to have a major scope for ageing-in-place and caregiving. This study examined the feasibility and cost-benefits of a personalized home automation intervention for care of individuals with Alzheimer's disease and related dementia (ADRD) with emphasis on their safety and independence, and reduction of care burden. METHODS: A mixed method intervention study was conducted with five dyads each comprising of a caregiver and care recipient with ADRD. Each dyad received personalized home automation intervention with individualized goals centred on home safety, activity engagement, and caregiver-recipient connectivity. The goals were created and monitored using goal attainment scaling (GAS). The dyads were followed up after three months with a home visit and interview. Goal attainment scoring and thematic analysis of the interviews were conducted to examine the findings from the study. RESULTS: All dyads indicated positive goal attainment between pre and post intervention. Goals that were met were complimented by the categories of peace of mind, self-efficacy, and care recipient engagement, while unmet goals as well as complexities with the technology were elucidated under challenges. Overarching and conceptually linked themes from the study were unfamiliarity, value, and timing. CONCLUSION: Home automation technology has the potential to be adapted to promote independence and safety of individuals with ADRD while relieving care burden. Nonetheless, we propose an early intervention framework to overcome identified challenges and optimize the technology's usability and sustainability.IMPLICATIONS FOR REHABILITATIONHome automation involving Internet of Things and Smart gadgets has gained rapid popularity owing to the comfort and connectivity they provide to mainstream consumers.The technology has an emerging significance to ageing-in-place and care of individuals with Alzheimer's Disease and Related Dementia (ADRD) as it can be adapted and implemented to enhance home safety and activity engagement of the care recipients while also strengthening their connectivity with the caregivers.Unfamiliarity with the technology compounded by the progression of ADRD can, however, be detrimental to its adoption.Individualized focus and early integration of the technology for caregiver-care recipient dyads can mitigate these challenges and optimize its usability and long-term value in relation to ageing-in-place and caregiver wellbeing.

Supporting Smart Home Scenarios Using OWL and SWRL Rules.

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SAREF.

Efficacy of Interventions Based on the Use of Information and Communication Technologies for the Promotion of Active Aging.

Nowadays, the study of how new media and technologies can be used to carry out health education by bringing these tools closer to the elderly population is interesting. It is a way of offering them access not only to greater knowledge, but to greater communication and relationship with their surroundings, a range of new possibilities and resources at their disposal that also represent a way to reduce the generation gap and bring them closer to the rest of the community. Objectives: to evaluate and analyze the studies that show the efficacy of interventions based on the use of information and communication technologies for the promotion of active aging in people older than or equal to 65 years who live in the community. Data sources, study eligibility criteria: the search for the articles was carried out from January 2012 to March 2021, in 6 databases (Pubmed, Cochrane Library, Scopus, Scielo, Google Academic and WOS) based on the clinical question, using the keywords derived from the DeCS and MeSH thesauri, combined with the Boolean operators "AND", "NOT" and "OR". The search was limited to publications from the last 9 years, in English and Spanish. Results: after applying the selection criteria and evaluating the quality of the methodology, 7.91% (n = 17) of the 215 results were included with filters: 7 systematic review, 5 of Cohorts and 5 of Randomized Controlled Trial. Conclusions and implications of key findings: the use of communication technologies reduces the feeling of loneliness, as well as the use of virtual reality to exercise, train memory or perform rehabilitation. The most difficult barrier to overcome is the prior ignorance of the majority of the elderly to the technology that is overcome by working as a team throughout the community, especially in the health and educational sector, as well as the family or social nucleus.

Effects of domotics on cognitive, social and personal functioning in patients with Parkinson's disease: A pilot study.

Home automation (HA) is either a "smart" house or a supportive environment, which enables the patients to regain an active role in daily life. HA could allow people affected by Parkinson Disease (PD) to better manage their daily lives. This study aims to evaluate the effects of domotics on quality of life, and personal/social autonomy in PD patients. We enrolled 40 with PD undergoing neurorehabilitation, who were randomized into either the control (CG) or the experimental group (EG). Two different rehabilitative approaches were used: the CG was submitted to a traditional training, whilst the EG underwent HA training, in which the activities were carried out through the use of assistive technologies. In both the training, the exercises were performed in small samples of 3-5 patients. Results showed that both CG and EG had a significant improvement in global cognitive functioning, executive functions, and instrumental autonomy. However, only in the EG, we observed a significant increase in social adaptation, activities of daily living and quality of life. This pilot study suggests that HA training could be a useful tool for the rehabilitation of patients with PD, improving social and cognitive functioning, personal autonomy and quality of life.

Towards Smart Home Automation Using IoT-Enabled Edge-Computing Paradigm.

Smart home applications are ubiquitous and have gained popularity due to the overwhelming use of Internet of Things (IoT)-based technology. The revolution in technologies has made homes more convenient, efficient, and even more secure. The need for advancement in smart home technology is necessary due to the scarcity of intelligent home applications that cater to several aspects of the home simultaneously, i.e., automation, security, safety, and reducing energy consumption using less bandwidth, computation, and cost. Our research work provides a solution to these problems by deploying a smart home automation system with the applications mentioned above over a resource-constrained Raspberry Pi (RPI) device. The RPI is used as a central controlling unit, which provides a cost-effective platform for interconnecting a variety of devices and various sensors in a home via the Internet. We propose a cost-effective integrated system for smart home based on IoT and Edge-Computing paradigm. The proposed system provides remote and automatic control to home appliances, ensuring security and safety. Additionally, the proposed solution uses the edge-computing paradigm to store sensitive data in a local cloud to preserve the customer's privacy. Moreover, visual and scalar sensor-generated data are processed and held over edge device (RPI) to reduce bandwidth, computation, and storage cost. In the comparison with state-of-the-art solutions, the proposed system is 5% faster in detecting motion, and 5 ms and 4 ms in switching relay on and off, respectively. It is also 6% more efficient than the existing solutions with respect to energy consumption.