Improved Screening of Fall Risk using Free-Living based Accelerometer Data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively

Identifying Elderly Patients at Risk of Falling using Time-Domain and Cyclostationarity Related Features

Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate and devastating consequences. Some falls can be prevented through interventions, proper management, and extra care. Therefore, studying and identifying elderly people with risk of falls is essential to minimize the falling risk and to minimize the severity of injuries that can occur from these falls. Besides, identifying at-risk patients can profoundly affect public health in a positive way. In this paper, we use classification techniques to identify at-risk patients using pressure signals of the innersoles of 520 elderly people. These people reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared: The first feature set includes time-domain, physiological, and cyclostationary features, whereas the second includes a subset of those features chosen by Relief-F as the most important features. Our study showed that the use of features from different walking conditions and using Relief-F as a feature selection method significantly improved the model prediction accuracy, i.e. by 5.24% from the best previously existing model. The results also point out that the mean and standard deviation of the stride time, gender, the degree of cyclostationarity were the most important features to include in classification models for the identification of elderly people at risk of falling

Developing Risk Assessment Tool for Patients’ In-Hospital Falls Using Predictive Modeling

Inpatient falls are a serious cause of fatal and non-fatal injuries among patients of all ages leading to disability and stillness. The post-fall treatment comes with rising medical costs and a stressful recovery phase. The present assessment tools align with analyzing causes of falls from historical data instead of present conditions. The key focus area of this research is to develop general-purpose fall risk assessment tools using machine learning-based predictive modeling. We used performance metrics to compare the accuracy and suggest the best suitable model for each shift. This general-purpose fall risk assessment tool can be used for all age groups in the diverse practice setting in hospitals. Considering the gap between the ideal fall risk assessment tool and conventional tools, our study includes factors such as etiology of falls, intrinsic, extrinsic, and situational risk factors. The analysis of patient falls will provide information to clinicians and guide them in developing intervention strategies for patients. A solid initiative to prevent falls and a tool that offers precise risk scores are critical pieces for improving outcomes of falls. Our research work has the potential to motivate the development of a general-purpose, highly accurate, and practical tool that can successfully determine an individual’s risk score

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Prediction of Fall Risk Among Community-Dwelling Older Adults Using a Wearable System

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor\u27s potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls

Personalized functional health and fall risk prediction using electronic health records and in-home sensor data

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data

Evaluation of Wearable Sensors as an Older Adult Fall Risk Assessment Tool

Falls are common in the geriatric population, with approximately one third of older adults falling each year. Falls can result in lasting physical and psychological consequences and cost approximately $20 billion per year in the United States. Wearable sensors can be used for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical care and older adult living environments. The objectives of this study were to evaluate eyes open and eyes closed static posturography in older adults; provide in-depth analysis of the differences between single-task and dual-task gait in elderly individuals and the relation to faller status; generate models for wearable-sensor-based fall risk classification in older adults and identify the optimal sensor type, location, combination, and modelling method for walking with and without a cognitive load task; and compare wearable-sensor-based fall risk classification performance to clinical assessment-based and posturography-based fall risk classification outcomes. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence; 47 non-fallers, 28 fallers based on 6 month prospective fall occurrence with retrospective fallers excluded) walked 7.62 m under single-task (ST) and dual-task (DT) conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, static posturography with eyes open and closed, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. Feature selection was performed using Relief-F, Fast Correlation-Based Filter (FCBF), and Correlation based Feature Selection (CFS). For static posturography, measures sensitive to anterior-posterior motion and medial-lateral centre of pressure (CoP) velocity were greater under eyes closed compared to eyes open conditions for prospective non-fallers, fallers, and multi-fallers. For prospective multi-fallers, medial-lateral range and root-mean square distance from the mean were also greater when visual input was removed, suggesting that assessment of medial-lateral balance control may be particularly important for evaluating the risk of multiple falls. Differences were found between prospective fallers and non-fallers for Romberg Quotient (RQ) anterior-posterior range and root-mean square distance from the mean. Differences between prospective multi-fallers and non-fallers were for eyes closed and RQ anterior-posterior and vector sum magnitude velocity. This suggests that RQ calculations are particularly relevant for elderly fall risk assessments. Measures that changed between ST and DT walking conditions, including non-temporal measures related to movement frequency and abnormal body segment movements, were identified. Increased gait variability under DT conditions was indicated by increased posterior CoP stance path deviations, medial-lateral CoP stance path deviation durations, and CoP stance path coefficient of variation; and decreased Fast Fourier Transform quartiles and ratio of even to odd harmonics. Decreased gait velocity and decreased pelvis and shank acceleration standard deviations (SD) could represent compensatory gait strategies to counter the increased gait variability and thus maintain stability. Differences between prospective fallers and prospective non-fallers were related to movement frequency and variability. Fall risk classification models that used Relief-F feature selection achieved the best performance. With feature selection, the best model for prospective faller classification contained ten features (four pressure-sensing insole features, six left shank accelerometer features) and used a support vector machine classifier. This model achieved an accuracy of 94%, F1 score of 0.923, and Matthew’s Correlation Coefficient (MCC) of 0.866. The posterior pelvis accelerometer provided strong single-sensor performance (83% accuracy, F1 score 0.769, MCC 0.645), although lower than the best multi-sensor model performance, and should be considered if a single-sensor system is necessary to reduce assessment cost and complexity at the point-of-care. Neural networks and support vector machines both achieved strong classification performance and outperformed naïve Bayesian classifiers. Sensor-based models outperformed clinical assessment-based models and posturography-based models for both retrospective and prospective fall risk classification. Wearable sensors provided strong fall risk classification performance and should be considered for point-of-care assessment of elderly fall risk

Coronectomy of deeply impacted lower third molar : incidence of outcomes and complications after one year follow-up

Objectives: The purpose of present study was to assess the surgical management of impacted third molar with proximity to the inferior alveolar nerve and complications associated with coronectomy in a series of patients undergoing third molar surgery. Material and Methods: The position of the mandibular canal in relation to the mandibular third molar region and mandibular foramen in the front part of the mandible (i.e., third molar in close proximity to the inferior alveolar nerve [IAN] or not) was identified on panoramic radiographs of patients scheduled for third molar extraction. Results: Close proximity to the IAN was observed in 64 patients (35 females, 29 males) with an impacted mandibular third molar. Coronectomy was performed in these patients. The most common complication was tooth migration away from the mandibular canal (n = 14), followed by root exposure (n = 5). Re-operation to remove the root was performed in cases with periapical infection and root exposure. Conclusions: The results indicate that coronectomy can be considered a reasonable and safe treatment alternative for patients who demonstrate elevated risk for injury to the inferior alveolar nerve with removal of the third molars. Coronectomy did not increase the incidence of damage to the inferior alveolar nerve and would be safer than complete extraction in situations in which the root of the mandibular third molar overlaps or is in close proximity to the mandibular canal