SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

Research on the Human Motion Recognition Method Based on Wearable

The accurate analysis of human dynamic behavior is very important for overcoming the limitations of movement diversity and behavioral adaptability. In this paper, a wearable device-based human dynamic behavior recognition method is proposed. The method collects acceleration and angular velocity data through a six-axis sensor to identify information containing specific behavior characteristics in a time series. A human movement data acquisition platform, the DMP attitude solution algorithm, and the threshold algorithm are used for processing. In this experiment, ten volunteers wore wearable sensors on their bilateral forearms, upper arms, thighs, calves, and waist, and movement data for standing, walking, and jumping were collected in school corridors and laboratory environments to verify the effectiveness of this wearable human movement recognition method. The results show that the recognition accuracy for standing, walking, and jumping reaches 98.33%, 96.67%, and 94.60%, respectively, and the average recognition rate is 96.53%. Compared with similar methods, this method not only improves the recognition accuracy but also simplifies the recognition algorithm and effectively saves computing resources. This research is expected to provide a new perspective for the recognition of human dynamic behavior and promote the wider application of wearable technology in the field of daily living assistance and health management.

A Rapid Response System for Elderly Safety Monitoring Using Progressive Hierarchical Action Recognition

The global trend of population aging presents an urgent challenge in ensuring the safety and well-being of elderly individuals, especially those living alone due to various circumstances. A promising approach to this challenge involves leveraging Human Action Recognition (HAR) by integrating data from multiple sensors. However, the field of HAR has struggled to strike a balance between accuracy and response time. While technological advancements have improved recognition accuracy, complex algorithms often come at the expense of response time. To address this issue, we introduce an innovative asynchronous detection method called Rapid Response Elderly Safety Monitoring (RESAM), which relies on progressive hierarchical action recognition and multi-sensor data fusion. Through initial analysis of inertial sensor data using Kernel Principal Component Analysis (KPCA) and multi-class classifiers, we efficiently reduce processing time and lower the false-negative rate (FNR). The inertial sensor identification serves as a pre-filter, enabling the identification of filtered abnormal signals. Decision-level data fusion is then executed, incorporating skeleton image analysis based on ResNet and the inertial sensor data from the initial step. This integration enables the accurate differentiation between normal and abnormal behaviors. The RESAM method achieves an impressive 97.4% accuracy on the UTD-MHAD database with a minimal delay of 1.22 seconds. On our internally collected database, the RESAM system attains an accuracy of 99%, ranking among the most accurate state-of-the-art methods available. These results underscore the practicality and effectiveness of our approach in meeting the critical demand for swift and precise responses in healthcare scenarios.

StresSense: Real-Time detection of stress-displaying behaviors

BACKGROUND

Wrist-worn gadgets like smartphones are ideal for unobtrusively gathering user data, in various fields such as health and fitness monitoring, communication, and productivity enhancement. They seamlessly integrate into users' daily lives, providing valuable insights and features without the need for constant attention or disruption. In sensitive domains like mental health, these devices provide user-friendly, privacy-protected means of diagnosis and treatment, offering a secure and cost-effective avenue for seeking help.

OBJECTIVES

This study addresses the limitations of traditional mental health assessment techniques, such as intrusive sensing and subjective self-reporting, by harnessing the unobtrusive data collection capabilities of smartphones. Equipped with accelerometers and other sensors, these devices offer a novel approach to mental health research. Our objective was to develop methods for real-time detection of stress and boredom behavior markers using smart devices and machine learning algorithms.

METHODOLOGY

By leveraging data from accelerometers (A), gyroscopes (G), and magnetometers (M), we compiled a dataset indicative of stress-related behaviors and trained various machine-learning models for predictive accuracy. The methodology involved collecting data from motion sensors (A, G, and M) on the dominant arm's wrist-worn smartphone, followed by data preprocessing, transformation from time series format, and training a Deep Neural Network (DNN) model for activity recognition.

FINDINGS

Remarkably, the DNN achieved an accuracy of 93.50% on test data, outperforming traditional and ensemble machine learning methods across different window sizes, and demonstrated real-time accuracy of 77.78%, validating its practical application.

CONCLUSION

In conclusion, this research presents a novel dataset for detecting stress and boredom behaviors using smartphones, reducing reliance on costly devices and offering a more objective assessment. It also proposes a DNN-based method for wrist-worn devices to accurately identify complex activities associated with stress and boredom, with benefits in terms of privacy and user convenience. This advancement represents a significant contribution to the field of mental health research, providing a less intrusive and more user-friendly approach to monitoring mental well-being.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Causes and global, regional, and national burdens of traumatic brain injury from 1990 to 2019

PURPOSE

Traumatic brain injury (TBI), currently a major global public health problem, imposes a significant economic burden on society and families. We aimed to quantify and predict the incidence and severity of TBI by analyzing its incidence, prevalence, and years lived with disability (YLDs). The epidemiological changes in TBI from 1990 to 2019 were described and updated to provide a reference for developing prevention, treatment, and incidence-reducing measures for TBI.

METHODS

A secondary analysis was performed on the incidence, prevalence, and YLDs of TBI by sex, age group, and region (n = 21,204 countries and territories) between 1990 and 2019 using the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Proportions in the age-standardized incidence rate due to underlying causes of TBI and proportions of minor and moderate or severe TBI were also reported.

RESULTS

In 2019, there were 27.16 million (95% uncertainty intervals (UI): 23.36 - 31.42) new cases of TBI worldwide, with age-standardized incidence and prevalence rates of 346 per 100,000 population (95% UI: 298-401) and 599 per 100,000 population (95% UI: 573-627), respectively. From 1990 to 2019, there were no significant trends in global age-standardized incidence (estimated annual percentage changes: -0.11%, 95% UI: -0.18% - -0.04%) or prevalence (estimated annual percentage changes: 0.01%, 95% UI: -0.04% - 0.06%). TBI caused 7.08 million (95% UI: 5.00 - 9.59) YLDs in 2019, with age-standardized rates of 86.5 per 100,000 population (95% UI: 61.1 - 117.2). In 2019, the countries with higher incidence rates were mainly distributed in Central Europe, Eastern Europe, and Australia. The 2019 global age-standardized incidence rate was higher in males than in females. The 2019 global incidence of moderate and severe TBI was 182.7 per 100,000 population, accounting for 52.8% of all TBI, with falls and road traffic injuries being the main causes in most regions.

CONCLUSIONS

The incidence of moderate and severe TBI was slightly higher in 2019, and TBI still accounts for a significant portion of the global injury burden. The likelihood of moderate to severe TBI and the trend of major injury under each injury cause from 1990 to 2019 and the characteristics of injury mechanisms in each age group are presented, providing a basis for further research on injury causes in each age group and the future establishment of corresponding policies and protective measures.

Wrist-Based Fall Detection: Towards Generalization across Datasets

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets.

New Horizons in artificial intelligence in the healthcare of older people

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context

Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S-O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).

Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique

In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.

Role of Telemedicine and Digital Technology in Public Health in India: A Narrative Review

There are still many areas of India without proper medical facilities. In such a setting, technology can play a facilitating role, particularly in reaching out to remote locations and offering a greater standard of care at a lower cost. The method of treating and diagnosing patients remotely through communication networks is known as telemedicine. When more patients get access to telemedicine, payers take more notice of how much less expensive it is than traditional medicine, and doctors are aware of its benefits. Telemedicine is a more beneficial technology that can expand access to preventive treatment and may lead to long-term health. Telemedicine has the potential to greatly affect public health. This paper reviews the current state of the art of telemedicine in India. Nearly 50 years ago, telemedicine was shrugged off as a complicated, expensive, and inefficient technology. Because of how quickly the information technology and telecommunications disciplines are advancing, telemedicine is today a viable, dependable, and useful technique. Practitioners and medical experts from a variety of fields have experienced success with telemedicine. The COVID-19 pandemic highlighted the need for strong primary healthcare networks for a more effective public health response during health emergencies and exposed the fragmentation of healthcare delivery systems. Although primary care is the first point of contact between the general public and the healthcare system, it has not recently grown much focus or funding. Even in the post-COVID-19 environment, telemedicine offers the potential to get through enduring barriers to primary care in India, such as a shortage of qualified medical professionals, issues with access, and the cost of in-person care. Telemedicine has the power to speed up the delivery of universal health coverage while strengthening primary care. There is a widening gap between people and those who offer basic health services as the population in India has grown, and the average lifespan has increased. Telemedicine helps with palliative care, early identification, a better cure, prevention, and rehabilitation in the treatment of cancer. Due to a shortage of primary care delivery networks and referral units, secondary and tertiary care facilities' health systems are overworked. To successfully use telemedicine, proper planning and operating processes are required. Thus, the development and implementation of telemedicine will improve patient care and India's primary healthcare system in the future. Finally, telemedicine's cost-effectiveness will likely be its most significant outcome.

Transfer Learning on Small Datasets for Improved Fall Detection

Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications.

Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs.

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.

An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM

Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network-4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters-were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

The Application and Prospect of Mobile Health (mHealth) in Health Service for Older People Living Alone in Community: A Narrative Review

As a result of improvements in life expectancy and reductions in fertility rate, the increasing world population ageing brings huge challenges for both developed and developing countries. Such factors as fewer children, migration of children and widowhood further increase the number of older people living alone. Older adults prefer age in place, which means care in the home. As the main place older people live in, care in community absolutely needs more attention. Optimizing health services for the elderly living in community is of positive significance to health promotion and happiness enhancement. But the traditional health service for the elderly has drawbacks of poor timeliness and high labor cost. The rapid development of modern science and technology make it possible to apply mHealth in health service for the elderly. At present, mHealth is relatively mature in the communities of developed countries. This article presents the application of mHealth in many developed countries, as references for developing countries.

A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.

Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study

Background

Older adults are at an increased risk of falls with the consequent impacts on the health of the individual and health expenditure for the population. Smartwatch apps have been developed to detect a fall, but their sensitivity and specificity have not been subjected to blinded assessment nor have the factors that influence the effectiveness of fall detection been fully identified.

Objective

This study aims to assess accuracy metrics for a novel fall detection smartwatch algorithm.

Methods

We performed a cross-sectional study of 22 healthy adults comparing the detection of induced forward, side (left and right), and backward falls and near falls provided by a smartwatch threshold-based algorithm, with a video record of induced falls serving as the gold standard; a blinded assessor compared the two. Three different smartwatches with two different operating systems were used. There were 226 falls: 64 were backward, 51 forward, 55 left sided, and 56 right sided.

Results

The overall smartwatch app sensitivity for falls was 77%, the specificity was 99%, the false-positive rate was 1.7%, and the false-negative rate was 16.4%. The positive and negative predictive values were 98% and 84%, respectively, while the accuracy was 89%. There were 249 near falls: the sensitivity was 89%, the specificity was 100%, there were no false positives, 11% were false negatives, the positive predictive value was 100%, the false-negative predictive value was 83%, and the accuracy was 93%.

Conclusions

Falls were more likely to be detected if the fall was on the same side as the wrist with the smartwatch. There was a trend toward some smartwatches and operating systems having superior sensitivity, but these did not reach statistical significance. The effectiveness data and modifying factors pertaining to this smartwatch app can serve as a reference point for other similar smartwatch apps.

Use of daily posture and activity tracking to assess sedentary behavior, toss-and-turns, and sleep duration of independently living Thai seniors

This study examines the postures and activities of elders using activity-monitoring device or diary booklet. The research focuses on using the tracked data to assess sedentary behaviors, toss-and-turns, and sleep duration. Fifty seniors participated in the study for 14 days to obtain anecdotal evidence: half of them wore Sookjai, a motion-tracking device; the other half recorded their activities manually via a diary. The results show that they spent most of their time in the sit/stand posture; they tossed and turned during naps and sleep. Both groups showed a similar pattern of activities: the higher level of sedentary behavior is related to a longer sleep duration. Sedentary behavior and naps increased the number of toss-and-turns at night; toss-and-turns did not affect sleep duration. These independent living adults rated themselves healthy regardless of the extent of their sedentary behaviors or tossing and turning. Although the device did not meet all expectations, the seniors did indicate a positive intention to use wearables.

The integration of mHealth technologies in telemedicine during the COVID-19 era: A cross-sectional study

Telemedicine is a rapidly expanding field of medicine and an alternative method for delivering quality medical care to patients' fingertips. With the COVID-19 pandemic, there has been an increase in the use of telemedicine to connect patients and healthcare providers, which has been made possible by mobile health (mHealth) applications. The goal of this study was to compare the satisfaction of patients with telemedicine among mHealth users and non-users. This was a survey-based study that included outpatients from Abu Dhabi. The association between patient satisfaction with telemedicine and use of mHealth technologies was described using regression models. This study included a total of 515 completed responses. The use of mHealth application was significantly associated with ease of booking telemedicine appointments (OR 2.61, 95% CI 1.63-4.18; P < .001), perception of similarity of quality of care between telemedicine consultations and in-person visits (OR 1.81, 95% CI 1.26-2.61; P = .001), and preference for using telemedicine applications over in-person visits during the COVID-19 pandemic (OR 1.74, 95% CI 1.12-2.72; P = .015). Our study results support that the use of mHealth applications is associated with increased patient satisfaction with telemedicine appointments.

Leveraging data science to enhance suicide prevention research: a literature review

OBJECTIVE

The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.

DESIGN

We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.

METHODS

For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.

RESULTS

Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.

CONCLUSION

Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls

Outdoor fall detection, in the context of accidents, such as falling from heights or in water, is a research area that has not received as much attention as other automated surveillance areas. Gathering sufficient data for developing deep-learning models for such applications has also proven to be not a straight-forward task. Normally, footage of volunteer people falling is used for providing data, but that can be a complicated and dangerous process. In this paper, we propose an application for thermal images of a low-cost rubber doll falling in a harbor, for simulating real emergencies. We achieve thermal signatures similar to a human on different parts of the doll’s body. The change of these thermal signatures over time is measured, and its stability is verified. We demonstrate that, even with the size and weight differences of the doll, the produced videos of falls have a similar motion and appearance to what is expected from real people. We show that the captured thermal doll data can be used for the real-world application of pedestrian detection by running the captured data through a state-of-the-art object detector trained on real people. An average confidence score of 0.730 is achieved, compared to a confidence score of 0.761 when using footage of real people falling. The captured fall sequences using the doll can be used as a substitute to sequences of people.

Application of artificial intelligence in wearable devices: Opportunities and challenges

BACKGROUND AND OBJECTIVES

Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data.

METHODS

Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives.

RESULTS

Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays.

CONCLUSION

The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.

Comparing fall detection methods in people with multiple sclerosis: A prospective observational cohort study

Background Falls occur across the population but are more common, and have more negative sequelae, in people with multiple sclerosis (MS). Given the prevalence and impact of falls, accurate measures of fall frequency are needed. This study compares the sensitivity and false discovery rates of three methods of fall detection: the current gold standard, prospective paper fall calendars, real-time self-reporting and automated detection, the latter two from a novel body-worn device. Methods Falls in twenty-five people with MS were recorded for eight weeks with prospective fall calendars, real-time body-worn self-report, and an automated body-worn detector concurrently. Eligible individuals were adults with MS enrolled in a randomized controlled trial of a fall prevention intervention. Entry criteria were at least two falls or near-falls in the previous two months, Expanded Disability Status Scale ≤ 6.0, community dwelling, and no MS relapse in the previous month. The sensitivity (proportion of true falls detected) and false discovery rates (proportion of false reports generated) of the fall detection methods were compared. A true fall was a fall reported by at least two methods. A false report was a fall reported by only one method. The trial is registered on ClinicalTrials.gov (NCT02583386) and is closed. Results In the 1,276 person-days of fall counting with all three methods in use simultaneously there were 1344 unique fall events. Of these, 8.5% (114) were true falls and 91.5% (1230) were false reports. Fall calendars had the lowest sensitivity (0.614) and the lowest false discovery rate (0.067). The automated detector had the highest sensitivity (0.921) and the highest false discovery rate (0.919). All methods generated under one false report per day. There were no fall detection-related adverse events. Conclusion Fall calendars likely underestimate fall frequency by around 40%. The automated detector evaluated here misses very few falls but likely overestimates the number of falls by around one fall per day. Additional research is needed to produce an ideal fall detection and counting method for use in clinical and research applications. Funding United States Department of Veterans Affairs, Rehabilitations Research and Development Service.

Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home

In many countries, the number of elderly people has grown due to the increase in the life expectancy of the population, many of whom currently live alone and are prone to having accidents that they cannot report, especially if they are immobilized. For this reason, we have developed a non-intrusive IoT device, which, through multiple integrated sensors, collects information on habitual user behavior patterns and uses it to generate unusual behavior rules. These rules are used by our SecurHome system to send alert messages to the dependent person’s family members or caregivers if their behavior changes abruptly over the course of their daily life. This document describes in detail the design and development of the SecurHome system.

A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

BACKGROUND

Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification.

METHODS

The system uses the smartphone's accelerometer and gyroscope to monitor the participants' motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller's location and activity before the fall.

RESULTS

In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles.

CONCLUSIONS

The system's performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Introduction

Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward.

Methods

Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set.

Results

Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%.

Conclusion

The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems-data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

User Acceptance of Smart Watch for Medical Purposes: An Empirical Study

This study aims to investigate the most effective and interesting variables that urge use of the smartwatch (SW) in a medical environment. To achieve this aim, the study was framed using an innovative and integrated research model, which is based on combining constructs from a well-established theoretical model’s TAM and other features that are critical to the effectiveness of SW which are content richness and personal innovativeness. The Technology Acceptance Model (TAM) is used to detect the determinants affecting the adoption of SW. The current study depends on an online questionnaire that is composed of (20) items. The questionnaire is distributed among a group of doctors, nurses, and administration staff in medical centers within the UAE. The total number of respondents is (325). The collected data were implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. The results of the current study show that the main constructs in the model contribute differently to the acceptance of SW. Based on the previous assumption, content richness and innovativeness are critical factors that enrich the user’s perceived usefulness. In addition, perceived ease of use was significantly predictive of either perceived usefulness or behavioral intention. Overall findings suggest that SW is in high demand in the medical field and is used as a common channel among doctors and their patients and it facilitates the role of transmitting information among its users. The outcomes of the current study indicate the importance of certain external factors for the acceptance of the technology. The genuine value of this study lies in the fact that it is based on a conceptual framework that emphasizes the close relationship between the TAM constructs of perceived usefulness and perceived ease of use to the construct of content richness, and innovativeness. Finally, this study helps us recognize the embedded motives for using SW in a medical environment, where the main motive is to enhance and facilitate the effective roles of doctors and patients.

Voice Assistant Application for Avoiding Sedentarism in Elderly People Based on IoT Technologies

The rise in the use of virtual assistants such as Siri, Google Assistant, or Alexa among different sectors of society is facilitating access to information and services that were previously inconceivable due to the existing digital divide due to age. This situation allows especially the elderly to perform tasks much more easily and to access applications and services that could be a challenge for them with other digital user interfaces. With this in mind, the EMERITI project aims to improve the lives of the elderly through the use of virtual assistants in different case studies. In this sense, virtual voice assistants along with the use of Internet of Things (IoT) technologies can contribute to avoid sedentarism in the elderly; however, it is necessary to address the problem of proactivity presented by the virtual assistants available in the market. This article presents a solution that, through the use of activity monitoring smart bracelets, IoT devices and virtual voice assistants allow the elderly to monitor their daily physical activity simply by using their voice and therefore prevent them from sedentary patterns. Finally, this study presents the technical results obtained after the deployment of the proposed system and discusses the main advantages and the current challenges of the use of virtual assistants in applications to prevent sedentary lifestyles in the elderly.

A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices

Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.

[Artificial intelligence in neurocritical care]

Artificial intelligence (AI) has been introduced into medicine and an AI-assisted medicine will be the future that we should help to shape. In particular, supervised, unsupervised, and reinforcement learning will be the main methods to play a role in the implementation of AI. Severely ill patients admitted to the intensive care unit (ICU) are closely monitored in order to be able to quickly respond to any changes. These monitoring data can be used to train AI models to predict critical phases in advance, making an earlier reaction possible. To achieve this a large amount of clinical data are needed in order to train models and an external validation on independent cohorts should take place. Prospective studies with treatment of patients admitted to the ICU with AI assistance should show that they provide a benefit for patients. We present the most important resources from de-identified (anonymized) patient data on open-source use for AI research in intensive care medicine. The focus is on neurological diseases in the ICU, therefore, we provide an overview of existing models for prediction of outcome, vasospasms, intracranial pressure and levels of consciousness. To introduce the advantages of AI in the clinical routine, more AI-based models with larger datasets will be needed. To achieve this international cooperation is absolutely necessary. Clinical centers associated with universities are needed to provide a constant validation of applied models as these models can change during use or a bias can develop during the training. A strong commitment to AI research is important for Germany, not only with respect to academic achievements but also in the light of a rapidly growing influence of AI on the economy.

Time-Frequency Analysis of Daily Activities for Fall Detection

Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of accelerometer-only activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2–3.5 Hz range and distinct from other rigorous activities such as running (3.5–5.5 Hz) and jumping (1–2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies.

Technology Utilization in Fall Prevention

ABSTRACT

Falls, defined as unplanned descents to the floor with or without injury to an individual, remain to be one of the most challenging health conditions. Fall rate is a key quality metric of acute care hospitals, rehabilitation settings, and long-term care facilities. Fall prevention policies with proper implementation have been the focus of surveys by regulatory bodies, including The Joint Commission and the Centers for Medicare and Medicaid Services, for all healthcare settings. Since October 2008, the Centers for Medicare and Medicaid Services has stopped reimbursing hospitals for the costs related to patient falls, shifting the accountability for fall prevention to the healthcare providers. Research shows that almost one-third of falls can be prevented and extensive fall prevention interventions exist. Recently, technology-based applications have been introduced in healthcare to obtain superior patient care outcomes and experience via efficiency, access, and reliability. Several areas in fall prevention deploy technology, including predictive and prescriptive analytics using big data, video monitoring and alarm technology, wearable sensors, exergame and virtual reality, robotics in home environment assessment, and personal coaching. This review discusses an overview of these technology-based applications in various settings, focusing on the outcomes of fall reductions, cost, and other benefits.

Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.