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

Inducing unstable walking conditions through visual and auditory stimuli

[Purpose] Falls can significantly affect elderly individuals. However, most current methods used to detect and analyze high-risk conditions make use of simulated falling movements for data collection, which may not accurately represent actual falls. The present study aimed to induce natural falls using visual and auditory stimuli to create unstable walking conditions. [Participants and Methods] Two experiments were performed. The first experiment focused on inducing unstable walking using visual stimuli; whereas, the second experiment combined visual and auditory stimuli. To investigate the effects of stimuli on the induction of unstable walking, our results were compared with those of normal walking conditions. In addition, the two experimental conditions were compared to identify the most effective stimuli. [Results] Both experiments revealed a decrease in step length, an increase in step time and width, and an increase in the coefficient of variation of measurements, indicating an induced walking pattern with a higher risk of falls. Furthermore, combining visual and auditory stimuli caused deterioration of inter-limb coordination, as observed through an increased phase coordination index, thus resulting in further instability during walking. [Conclusion] Visual and auditory stimuli induced unstable walking. In particular, the combination of visual and auditory stimuli with a 0.8-s rhythm increased instability.

Development of artificial intelligence edge computing based wearable device for fall detection and prevention of elderly people

Elderly falls are a major concerning threat resulting in over 1.5-2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long-term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people. Further, the various deep learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) are utilized for activity recognition of elderly. Also, the CNN-LSTM, RNN-LSTM and GRU-LSTM with and without attention layer respectively are utilized and the performance metrics are analyzed to find the best deep learning model. Furthermore, the three different hardware boards such as Jetson Nano developer board, Raspberry PI 3 and 4 are utilized as an AI edge computing device and the best deep learning model is implemented and the computation time is evaluated. Results demonstrate that the CNN-LSTM with attention layer exhibits the accuracy, recall, precision and F1_Score of 97%, 98%, 98% and 0.98 respectively which is better when compared to other deep learning models. Also, the computation time of NVIDIA Jetson Nano is less when compared to other edge computing devices. This work appears to be of high societal relevance since the proposed wearable device can be used to monitor the activity of elderly and prevents the elderly falls which improve the quality of life of elderly people.

The effectiveness of a fall detection device in older nursing home residents: a pilot study

BACKGROUND

Real-world research to evaluate the effect of device technology in preventing fall-related morbidity is limited. This pilot study aims to investigate the effectiveness of a non-wearable fall detection device in older nursing home residents.

METHODS

The study was conducted in a nursing home with single-resident rooms. Fall detection devices were randomly set up in half of the rooms. Demographic data, comorbidities, lists of medications, and functional, nutritional, and frailty status were recorded. The residents were followed up for 3 months. The primary outcome was falls and the secondary outcome was all-cause mortality.

RESULTS

A total of 26 participants were enrolled in the study. The study group consisted of 13 residents who had a fall detection device in their rooms. The remaining 13 residents on the same floor formed the control group. Participants had a mean age of 82 ± 10 years and 89% of the residents were female. The most prevalent comorbidity was dementia. Two residents from the control group and one resident from the study group experienced a fall event during follow-up. The fall events in the control group were identified retrospectively by the nursing home staff, whereas the fall in the study group received a prompt response from the staff who were notified by the alarm. One resident was transferred to the hospital and died due to a non-fall related reason.

CONCLUSION

Device technology may provide an opportunity for timely intervention to prevent fall-related morbidity in institutionalized older adults.

Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.

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.

Towards human-centered AI and robotics to reduce hospital falls: finding opportunities to enhance patient-nurse interactions during toileting

Introduction: Patients who are hospitalized may be at a higher risk for falling, which can result in additional injuries, longer hospitalizations, and extra cost for healthcare organizations. A frequent context for these falls is when a hospitalized patient needs to use the bathroom. While it is possible that "high-tech" tools like robots and AI applications can help, adopting a human-centered approach and engaging users and other affected stakeholders in the design process can help to maximize benefits and avoid unintended consequences. Methods: Here, we detail our findings from a human-centered design research effort to investigate how the process of toileting a patient can be ameliorated through the application of advanced tools like robots and AI. We engaged healthcare professionals in interviews, focus groups, and a co-creation session in order to recognize common barriers in the toileting process and find opportunities for improvement. Results: In our conversations with participants, who were primarily nurses, we learned that toileting is more than a nuisance for technology to remove through automation. Nurses seem keenly aware and responsive to the physical and emotional pains experienced by patients during the toileting process, and did not see technology as a feasible or welcomed substitute. Instead, nurses wanted tools which supported them in providing this care to their patients. Participants envisioned tools which helped them anticipate and understand patient toileting assistance needs so they could plan to assist at convenient times during their existing workflows. Participants also expressed favorability towards mechanical assistive features which were incorporated into existing equipment to ensure ubiquitous availability when needed without adding additional mass to an already cramped and awkward environment. Discussion: We discovered that the act of toileting served more than one function, and can be viewed as a valuable touchpoint in which nurses can assess, support, and encourage their patients to engage in their own recovery process as they perform a necessary and normal function of life. While we found opportunities for technology to make the process safer and less burdensome for patients and clinical staff alike, we believe that designers should preserve and enhance the therapeutic elements of the nurse-patient interaction rather than eliminate it through automation.

Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.

Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning

Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

Development of a Machine Learning-Based Model to Predict Timed-Up-and-Go Test in Older Adults

INTRODUCTION

The measurement of physical frailty in elderly patients with orthopedic impairments remains a challenge due to its subjectivity, unreliability, time-consuming nature, and limited applicability to uninjured individuals. Our study aims to address this gap by developing objective, multifactorial machine models that do not rely on mobility data and subsequently validating their predictive capacity concerning the Timed-up-and-Go test (TUG test) in orthogeriatric patients.

METHODS

We utilized 67 multifactorial non-mobility parameters in a pre-processing phase, employing six feature selection algorithms. Subsequently, these parameters were used to train four distinct machine learning algorithms, including a generalized linear model, a support vector machine, a random forest algorithm, and an extreme gradient boost algorithm. The primary goal was to predict the time required for the TUG test without relying on mobility data.

RESULTS

The random forest algorithm yielded the most accurate estimations of the TUG test time. The best-performing algorithm demonstrated a mean absolute error of 2.7 s, while the worst-performing algorithm exhibited an error of 7.8 s. The methodology used for variable selection appeared to exert minimal influence on the overall performance. It is essential to highlight that all the employed algorithms tended to overestimate the time for quick patients and underestimate it for slower patients.

CONCLUSION

Our findings demonstrate the feasibility of predicting the TUG test time using a machine learning model that does not depend on mobility data. This establishes a basis for identifying patients at risk automatically and objectively assessing the physical capacity of currently immobilized patients. Such advancements could significantly contribute to enhancing patient care and treatment planning in orthogeriatric settings.

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite.

Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method

Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.

Joint behaviour during arm swing changes with gait speed and predicts spatiotemporal variability and dynamic stability in healthy young adults

BACKGROUND

Arm swing is linked to gait stability. How this is accomplished is unclear as most investigations artificially manipulate arm swing amplitude and examine average patterns. Biomechanical evaluation of stride-to-stride upper limb behaviour across a range of gait speeds, where the arm swings as preferred, could clarify this link.

RESEARCH QUESTION

How do stride-to-stride arm swing behaviours change with gait speed and relate to stride-to-stride gait fluctuations?

METHODS

Young adults (n = 45, 25 females) completed treadmill gait at preferred, slow (70% of preferred), and fast speed (130% of preferred) while full-body kinematics were acquired with optoelectronic motion capture. Arm swing behaviour was quantified by shoulder, elbow, and wrist joint angle amplitude (range of motion [ROM]) and motor variability (e.g. mean standard deviation [meanSD], local divergence exponent [λ_max]). Stride-to-stride gait fluctuation was quantified by spatiotemporal variability (e.g. stride time CV) and dynamic stability (i.e. trunk local dynamic stability [trunk λ_max], centre-of-mass smoothness [COM HR]). Repeated measures ANOVAs tested for speed effects and step-wise linear regressions identified arm swing-based predictors of stride-to-stride gait fluctuation.

RESULTS

Speed decreased spatiotemporal variability and increased trunk λ_max and COM HR in the anteroposterior and vertical axes. Adjustments in gait fluctuations occurred with increased upper limb ROM, particularly for elbow flexion, and increased meanSD and λ_max of shoulder, elbow, and wrist angles. Models of upper limb measures predicted 49.9-55.5% of spatiotemporal variability and 17.7-46.4% of dynamic stability. For dynamic stability, wrist angle features were the best and most common independent predictors.

SIGNIFICANCE

Findings highlight that all upper limb joints, and not solely the shoulder, underlie changes in arm swing amplitude, and that arm swing strategies pair with the trunk and contrast with centre-of-mass and stride strategies. Findings suggest that young adults search for flexible arm swing motor strategies to help optimize stride consistency and gait smoothness.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

A Classification Method for Workers' Physical Risk

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review

In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators.

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F₁-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.

Artificial intelligence for falls management in older adult care: A scoping review of nurses' role

AIM

This study aims to synthesize evidence on nurses' involvement in artificial intelligence research for managing falls in older adults.

BACKGROUND

Artificial intelligence techniques are used to analyse health datasets to aid clinical decision making, patient care and service delivery but nurses' involvement in this area of research for managing falls in older adults remains unknown.

EVALUATION

A scoping review was conducted. CINAHL, the Cochrane Library, Embase, MEDLI and PubMed were searched. Results were screened against inclusion criteria. Relevant data were extracted, and studies summarized using a descriptive approach.

KEY ISSUES

The evidence shows many artificial intelligence techniques, particularly machine learning, are used to identify falls risk factors and build predictive models that could help prevent falls in older adults, with nurses leading and participating in this research.

CONCLUSION

Further rigorous experimental research is needed to determine the effectiveness of algorithms in predicting aspects of falls in older adults and how to implement artificial intelligence tools in gerontological nursing practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurses should pursue interdisciplinary collaborations and educational opportunities in artificial intelligence, so they can actively contribute to research on falls management. Nurses should facilitate the collection of digital falls datasets to support this emerging research agenda and the care of older adults.

Preventing Patient Falls Overnight Using Video Monitoring: A Clinical Evaluation

Inpatient falls are devastating for patients and their families and an ongoing problem for healthcare providers worldwide. Inpatient falls overnight are particularly difficult to predict and prevent. The aim of this cohort study was to evaluate effectiveness of overnight portable video monitoring as an adjunct falls prevention strategy for high falls risk patients in inpatient clinical units. Over three months, three clinical inpatient wards were provided with baby monitor equipment to facilitate portable video monitoring. Portable video monitoring registers were completed nightly and nursing staff were invited to complete surveys (n = 31) to assess their experiences of using portable video monitoring. A total of 494 episodes of portable video monitoring were recorded over the three-month period, with clinical areas reporting a total of four inpatient falls from monitoring participants (0.8% of total portable video monitoring episodes). Overall, there was a statistically significant reduction in total inpatient falls overnight on the target wards. Surveyed nursing staff reported feeling better equipped to prevent falls and indicated they would like to continue using portable monitoring as a falls prevention strategy. This study provides evidence to support the use of portable video monitoring as an effective falls prevention strategy in the hospital environment.

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

BACKGROUND

Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls.

METHODS

We used the Kinect^® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation.

RESULTS

The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%.

CONCLUSIONS

This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling

The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with noninvasive and cost-effective merits, have been widely used to monitor various health conditions. However, the dynamic and heterogeneous pattern of ECG signals makes relevant feature construction and predictive model development a challenging task. In this study, we aim to develop an integrated approach for one-day-forward wellness prediction in the community-dwelling elderly using single-lead short ECG signal data via multiple-features construction and predictive model implementation. Vital signs data from the elderly were collected via station-based equipment on a daily basis. After data preprocessing, a set of features were constructed from ECG signals based on the integration of various models, including time and frequency domain analysis, a wavelet transform-based model, ensemble empirical mode decomposition (EEMD), and the refined composite multiscale sample entropy (RCMSE) model. Then, a machine learning based predictive model was established to map the l-day lagged features to wellness condition. The results showed that the approach developed in this study achieved the best performance for wellness prediction in the community-dwelling elderly. In practice, the proposed approach could be useful in the timely identification of elderly people who might have health risks, and could facilitating decision-making to take appropriate interventions.

A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research

BACKGROUND

Falls in older adults are a significant and growing public health concern. There are multiple risk factors associated with falls that may be addressed within the scope of chiropractic training and licensure. Few attempts have been made to summarize existing evidence on multimodal chiropractic care and fall risk mitigation. Therefore, the broad purpose of this review was to summarize this research to date. BODY: Systematic review was conducted following PRISMA guidelines. Databases searched included PubMed, Embase, Cochrane Library, PEDro, and Index of Chiropractic Literature. Eligible study designs included randomized controlled trials (RCT), prospective non-randomized controlled, observational, and cross-over studies in which multimodal chiropractic care was the primary intervention and changes in gait, balance and/or falls were outcomes. Risk of bias was also assessed using the 8-item Cochrane Collaboration Tool. The original search yielded 889 articles; 21 met final eligibility including 10 RCTs. One study directly measured the frequency of falls (underpowered secondary outcome) while most studies assessed short-term measurements of gait and balance. The overall methodological quality of identified studies and findings were mixed, limiting interpretation regarding the potential impact of chiropractic care on fall risk to qualitative synthesis.

CONCLUSION

Little high-quality research has been published to inform how multimodal chiropractic care can best address and positively influence fall prevention. We propose strategies for building an evidence base to inform the role of multimodal chiropractic care in fall prevention and outline recommendations for future research to fill current evidence gaps.

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

Backgrounds

Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence.

Materials and methods

Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression.

Results

From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy.

Conclusion

This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer's falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose-LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer's falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

Deep Learning enabled Fall Detection exploiting Gait Analysis

Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434x2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives.

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.

Human Activity Recognition by Sequences of Skeleton Features

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

Falls in older adults: a practical approach

BACKGROUND

Falls are a major problem in public health since they are an important cause of morbidity and mortality. To evaluate the risk of fall and prescribe preventive interventions may be a challenging task.

OBJECTIVES

The objectives of this study are to summarize the most relevant information on the topic "falls in the elderly" and to give a critical view and practical clinical approach on this topic.

METHODS

In March 2022, a search of Pubmed database was performed, using the terms "fall elderly", fall prevention", "fall risk", with the following parameters: five years, review, systematic review, meta-analysis, practice guidelines.

RESULTS

There are several risk factors for falls that can be grouped in different areas (psychosocial, demographic, medical, medication, behavioral, environmental). The clinical evaluation of an older adult prone to falls must include identification of risk factors through history and examination and identification of risk of falls through an assessment tool such as gait velocity, functional reach test, timed up and go, Berg balance test, and miniBEST test. Fall prevention strategies can be single or multiple, and physical activity is the most cited. Technology can be used to detect and prevent falls.

CONCLUSION

A systematic approach to the older patient in risk of falls is feasible and may impact fall prevention.

Identification of the cause of fall during the pre-impact fall period

[Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant’s waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0–2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method.

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.