Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis

Background

Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction.

Objective

This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots.

Results

A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations).

Conclusions

Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

The role of digital transformation in addressing health inequalities in coastal communities: barriers and enablers

Healthcare systems worldwide are striving for the "quadruple aim" of better population health and well-being, improved experience of care, healthcare team well-being (including that of carers) and lower system costs. By shifting the balance of care from reactive to preventive by facilitating the integration of data between patients and clinicians to support prevention, early diagnosis and care at home, many technological solutions exist to support this ambition. Yet few have been mainstreamed in the NHS. This is particularly the case in English coastal areas which, despite having a substantially higher burden of physical and mental health conditions and poorer health outcomes, also experience inequalities with respect to digital maturity. In this paper, we suggest ways in which digital health technologies (DHTs) can support a greater shift towards prevention; discuss barriers to digital transformation in coastal communities; and highlight ways in which central, regional and local bodes can enable transformation. Given a real risk that variations in digital maturity may be exacerbating coastal health inequalities, we call on health and care policy leaders and service managers to understands the potential benefits of a digital future and the risks of failing to address the digital divide.

Perspectives on the popularization of smart senior care to meet the demands of older adults living alone in communities of Southwest China: A qualitative study

Background

Older adults who live alone face challenges in daily life and in maintaining their health status quo. Currently, however, their growing demands cannot be satisfied with high quality; therefore, these demands expressed by elders may be settled in the form of smart senior care. Hence, the improvement in smart senior care may produce more positive meanings in promoting the health and sense of happiness among this elderly population. This study aimed to explore the perceptions of demands and satisfaction with regard to the provision of senior care services to the community-dwelling older adults who live alone in Southwest China, thus providing a reference for the popularization of smart senior care.

Methods

This study adopted a qualitative descriptive approach on demands and the popularization of smart senior care. Semi-structured and in-depth individual interviews were conducted with 15 community-dwelling older adults who lived alone in Southwest China between March and May 2021. Thematic analysis was applied to analyze the data.

Results

Through data analysis, three major themes and subcategories were generated: "necessities" (contradiction: more meticulous daily life care and higher psychological needs vs. the current lower satisfaction status quo; conflict: higher demands for medical and emergency care against less access at present), "feasibility" (objectively feasible: the popularization of smart devices and applications; subjectively feasible: interests in obtaining health information), and "existing obstacles" (insufficient publicity; technophobia; patterned living habits; and concerns).

Conclusions

Smart senior care may resolve the contradiction that prevails between the shortage of medical resources and the increasing demands for eldercare. Despite several obstacles that stand in the way of the popularization of smart senior care, the necessities and feasibility lay the preliminary foundation for its development and popularization. Decision-makers, communities, developers, and providers should cooperate to make smart senior care more popular and available to seniors living alone, facilitating independence while realizing aging in place by promoting healthy aging.

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

Methodological Quality of User-Centered Usability Evaluation of Ambient Assisted Living Solutions: A Systematic Literature Review

This study aimed to determine the methodological quality of user-centered usability evaluation of Ambient Assisted Living (AAL) solutions by (i) identifying the characteristics of the AAL studies reporting on user-centered usability evaluation, (ii) systematizing the methods, procedures and instruments being used, and (iii) verifying if there is evidence of a common understanding on methods, procedures, and instruments for user-centered usability evaluation. An electronic search was conducted on Web of Science, Scopus, and IEEE Xplore databases, combining relevant keywords. Then, titles and abstracts were screened against inclusion and exclusion criteria, and the full texts of the eligible studies were retrieved and screened for inclusion. A total of 44 studies were included. The results show a great heterogeneity of methods, procedures, and instruments to evaluate the usability of AAL solutions and, in general, the researchers fail to consider and report relevant methodological aspects. Guidelines and instruments to assess the quality of the studies might help improving the experimental design and reporting of studies on user-centered usability evaluation of AAL solutions.

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

Design and Implementation of the E-Switch for a Smart Home

As the development of systems in smart homes is increasing, it is of ever-increasing importance to have data, which artificial intelligence methods and techniques can apply to recognize activities and patterns or to detect anomalies, with the aim of reducing energy consumption in the main home domestic services, and to offer users an alternative in the management of these resources. This paper describes the design and implementation of a platform based on the internet of things and a cloud environment that allows the user to remotely control and monitor Wi-Fi wireless e-switch in a home through a mobile application. This platform is intended to represent the first step in transforming a home into a smart home, and it allows the collection and storage of the e-switch information, which can be used for further processing and analysis.

Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today's society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

Smart@home - supporting safety and mobility of elderly and care dependent people in their own homes through the use of technical assistance systems and conventional mobility supporting tools: a cross-sectional survey

Background

The use of technical solutions and conventional mobility supporting aids can support the independence of people into old age in their own homes. However, we found relatively few empirical investigations on the effects and costs of these systems.

Methods

The aim of the study was to investigate usability, user satisfaction and the correlation between costs and benefits of different built-in smart home solutions and conventional mobility supporting tools in the home of elderly, partially care-dependent tenants (> 65 years). A cross-sectional survey was conducted from February to March 2018 with tenants of a housing association in apartments equipped with smart home technology and conventional mobility supporting tools. The response rate in the intervention group was n = 37 persons (out of 46 tenants with installed smart home and conventional solutions) and in the control group n = 64 persons (out of 100 tenants without built-in smart home and conventional solutions). Data were collected by a written questionnaire regarding usability and satisfaction of the tenants with the built-in smart home solutions and conventional mobility supporting tools. In addition, both the intervention and the control group were asked general questions about communication, safety and how to deal with the need for long-term care in their own living environment.

Results

Results showed that with regard to usability, satisfaction and price performance ratio of the installed smart home solutions, the installation of the corresponding solutions with an overall score of 1.41 (on a scale of 1 (very good) to 6 (unsatisfactory)) was mostly positively evaluated by the tenants. Overall, users rated the installed smart home solutions better than the conventional mobility supporting tools (such as handholds and increased balcony floor level).

Conclusions

Analysis of the price performance ratio showed that smart home solutions are generally more expensive than conventional tools, but also contribute significantly to an increased security of the tenants, and thus may enable longer living in a familiar environment. We recommend modularized offers consisting of various components of smart home solutions, since this significantly reduces installation costs and allows for an individual composition according to requirements. Moreover, smart home solutions should be considered to be listed as medical aids.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02118-9.

A Feasibility Study of Expanded Home-Based Telerehabilitation After Stroke

Introduction: High doses of activity-based rehabilitation therapy improve outcomes after stroke, but many patients do not receive this for various reasons such as poor access, transportation difficulties, and low compliance. Home-based telerehabilitation (TR) can address these issues. The current study evaluated the feasibility of an expanded TR program. Methods: Under the supervision of a licensed therapist, adults with stroke and limb weakness received home-based TR (1 h/day, 6 days/week) delivered using games and exercises. New features examined include extending therapy to 12 weeks duration, treating both arm and leg motor deficits, patient assessments performed with no therapist supervision, adding sensors to real objects, ingesting a daily experimental (placebo) pill, and generating automated actionable reports. Results: Enrollees (n = 13) were median age 61 (IQR 52-65.5), and 129 (52-486) days post-stroke. Patients initiated therapy on 79.9% of assigned days and completed therapy on 65.7% of days; median therapy dose was 50.4 (33.3-56.7) h. Non-compliance doubled during weeks 7-12. Modified Rankin scores improved in 6/13 patients, 3 of whom were >3 months post-stroke. Fugl-Meyer motor scores increased by 6 (2.5-12.5) points in the arm and 1 (-0.5 to 5) point in the leg. Assessments spanning numerous dimensions of stroke outcomes were successfully implemented; some, including a weekly measure that documented a decline in fatigue (p = 0.004), were successfully scored without therapist supervision. Using data from an attached sensor, real objects could be used to drive game play. The experimental pill was taken on 90.9% of therapy days. Automatic actionable reports reliably notified study personnel when critical values were reached. Conclusions: Several new features performed well, and useful insights were obtained for those that did not. A home-based telehealth system supports a holistic approach to rehabilitation care, including intensive rehabilitation therapy, secondary stroke prevention, screening for complications of stroke, and daily ingestion of a pill. This feasibility study informs future efforts to expand stroke TR. Clinical Trial Registration: Clinicaltrials.gov, # NCT03460587.

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model's generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

Sensor Technology for Smart Homes

As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged [...].

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.