scholarly journals Classification of Daily Activities for the Elderly Using Wearable Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Liu ◽  
Jeehoon Sohn ◽  
Sukwon Kim
Keyword(s):  
Daily Living ◽  
Wearable Sensors ◽  
The Elderly ◽  
Daily Activities ◽  
Elderly Subjects ◽  
Activity Levels ◽  
Inertial Measurement ◽  
Healthy Elderly ◽  
Measurement Units

Monitoring of activities of daily living (ADL) using wearable sensors can provide an objective indication of the activity levels or restrictions experienced by patients or elderly. The current study presented a two-sensor ADL classification method designed and tested specifically with elderly subjects. Ten healthy elderly were involved in a laboratory testing with 6 types of daily activities. Two inertial measurement units were attached to the thigh and the trunk of each subject. The results indicated an overall rate of misdetection being 2.8%. The findings of the current study can be used as the first step towards a more comprehensive activity monitoring technology specifically designed for the aging population.

The influence of dynamic visual environments on postural sway in the elderly

1999 ◽  
Vol 9 (3) ◽  
pp. 197-205
Author(s):  
L.L. Borger ◽  
S.L. Whitney ◽  
M.S. Redfern ◽  
J.M. Furman
Keyword(s):  
Visual Information ◽  
Postural Sway ◽  
Center Of Pressure ◽  
Age Groups ◽  
The Elderly ◽  
Visual Scene ◽  
Elderly Subjects ◽  
Mean Velocity ◽  
Healthy Elderly ◽  
Young Subjects

Postural sway during stance has been found to be sensitive to moving visual scenes in young adults, children, and those with vestibular disease. The effect of visual environments on balance in elderly individuals is relatively unknown. The purpose of this study was to compare postural sway responses of healthy elderly to those of young subjects when both groups were exposed to a moving visual scene. Peak to peak, root mean squared, and mean velocity of the center of pressure were analyzed under conditions combining four moving scene amplitudes ( 2 . 5 ∘ , 5 ∘ , 7 . 5 ∘ , 10 ∘ ) and two frequencies of scene movement (0.1 Hz, 0.25 Hz). Each visual condition was tested with a fixed floor and sway referenced platform. Results showed that elderly subjects swayed more than younger subjects when experiencing a moving visual scene under all conditions. The elderly were affected more than the young by sway referencing the platform. The differences between the two age groups were greater at increased amplitudes of scene movement. These results suggest that elderly are more influenced by dynamic visual information for balance than the young, particularly when cues from the ankles are altered.

scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

scholarly journals Energy Requirement Equations in Elderly Patients: A Systematic Review (P01-002-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Dario Gregori ◽  
Honoria Ocagli ◽  
Corrado Lanera ◽  
Giulia Lorenzoni
Keyword(s):  
Systematic Review ◽  
Elderly Patients ◽  
Indirect Calorimetry ◽  
Gold Standard ◽  
Web Application ◽  
The Elderly ◽  
Energy Requirements ◽  
Elderly Subjects ◽  
Predictive Equation ◽  
Healthy Elderly

Abstract Objectives Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. In the clinical setting, predictive equations are widely used to estimate the Resting Energy Expenditure (REE). Although easy to use, these equations are not always validated for the elderly and, even if validated, they often provide different outputs of energy requirements for the same subject. This study aimed at doing a systematic review of the equations for the estimation of REE in the elderly with the final aim of developing a web-based application helping clinicians in finding out the most appropriate equation for estimating the REE for each subject. Methods The systematic review was carried out using PubMed and Scopus following PRISMA guidelines. Studies in subjects older than 65 years of age, testing the performance of a predictive equation for the estimation of REE vs. a gold standard (indirect calorimetry or doubly labeled water) were included in the review. Studies performed in critically ill elderly patients were excluded. Results The initial search identified 2035 studies. The final review included 50 studies. Included studies were mainly observational, conducted in healthy elderly subjects enrolled in the outpatient setting, and using indirect calorimetry as gold standard. The 50 studies included in the review corresponded to 189 different equations. Several parameters were included in the equations and they can be divided as following: anthropometric characteristics, body composition parameters, environmental measures, laboratory tests, presence of comorbidities, and physical activity frequency. Conclusions The assessment of the energy requirements in the elderly is crucial for the management of nutritional problems in this population group since nutritional problems are related to worse health outcomes. The present study showed a wide use of different type of equations for the estimation of REE in the elderly highlighting the need of choosing the most appropriate predictive equation according to the subject characteristics and health status. The web application that is currently under development will help clinicians in doing that. Funding Sources Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Padova, Italy.

scholarly journals Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 109
Author(s):  
Binbin Su ◽  
Christian Smith ◽  
Elena Gutierrez Farewik
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Measurement Unit ◽  
Deep Convolutional Neural Networks ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Gait Phase ◽  
Robotic Devices ◽  
Phase Recognition ◽  
Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

scholarly journals Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5283 ◽  
Author(s):  
Gianmarco Baldini ◽  
Filip Geib ◽  
Raimondo Giuliani
Keyword(s):  
Wearable Sensors ◽  
Measurement Unit ◽  
Daily Routine ◽  
Inertial Measurement Units ◽  
Digital Output ◽  
Inertial Measurement ◽  
Continuous Authentication ◽  
Measurement Units ◽  
Analytical Approaches ◽  
Road Segments

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments.

scholarly journals The real-time elderly fall posture identifying scheme with wearable sensors

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988561
Author(s):  
Tao Xu ◽  
Wei Sun ◽  
Shaowei Lu ◽  
Ke-ming Ma ◽  
Xiaoqiang Wang
Keyword(s):  
Activities Of Daily Living ◽  
Real Time ◽  
Daily Living ◽  
Wearable Sensors ◽  
Smart Phone ◽  
The Elderly ◽  
The Body ◽  
Short Message ◽  
Triaxial Accelerometer ◽  
Time Fall

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

scholarly journals P0806RESIDUAL RENAL FUNCTION IS COMPLEMENTARY TO GERIATRIC ASSESSMENT SCORE IN ASSESSING CLINICAL RISK OF ELDERLY PATIENTS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Alberto Castagna ◽  
Davide Bolignano ◽  
Irma Figlia ◽  
Rosa Paola Cerra ◽  
Carmen Ruberto ◽  
...  
Keyword(s):  
Renal Function ◽  
Risk Stratification ◽  
Activities Of Daily Living ◽  
Geriatric Assessment ◽  
Comprehensive Geriatric Assessment ◽  
Daily Living ◽  
The Elderly ◽  
Residual Renal Function ◽  
Elderly Subjects ◽  
Old Patients

Abstract Background and Aims Renal function impairment is highly pervasive in the elderly and triggers increased morbidity and mortality. Comprehensive geriatric assessment (CGA) is a validated multidisciplinary instrument to assess medical, psychosocial and functional limitations of old patients with diagnostic and risk-stratification purposes. In a focused cohort of frail individuals, we aimed at evaluating possible relationships between single CGA items and renal function. Method 254 consecutive elderly subjects (mean age 79.9±6.6 years, female 65.8%) from the geriatric division of a large Italian community hospital were studied. We collected clinical data including CGA and renal function (CKD-EPI formula). CGA single items included the Cumulative Illness Rating Scale (CIRS), the Exton Smith Scale (ESS), the Mini Nutritional Assessment Short Form (MNA-SF), the Katz‘s Activities of Daily Living (ADL), the Lawton’s Instrumental Activities of Daily Living (IADL), the Short Portable Mental Status Questionnaire (SPMSQ) and the amount of drugs administered (AD). Results Mean eGFR was 66.37±30.94 mL/min/1.73 m2. Overall, the reported CIRS, ESS, MNA, ADL and AD scores were low (7.6±3.3) while IADL and SPMQ were on a mild range, denoting a potential alarm signal for poor prognosis and the risk for adverse outcomes. At univariate analyses, eGFR was significantly associated with CIRS (R=-0.389, p<0.0001), ESS (R=0.355, p<0.0001), MNA (R=0.394, p<0.0001), ADL (R=0.394, p<0.0001), AD (R=-0.374, p<0.0001. while a weak, although significant correlation was found with IADL (R=0.131, p=0.038) and SPMSQ (R=-0.141, p=0.038). In a fully adjusted multivariate analyses only SPMSQ (ß=-0.174, p=0.04), ADL (ß=0.182, p=0.012), IADL (ß =0.209, p=0.003) and AD (ß=-0.354, p<0.0001) remained significant predictors of residual renal function. Conclusion In elderly frail subjects, residual renal function may influence daily life and cognitive activities, the perceived quality of living and the entity of drug assumption. Inclusion of renal function within a comprehensive geriatric assessment could help improving risk stratification in the elderly

Scales for Mental State and Daily Living Activities for the Elderly: Clinical Behavioral Scales for Assessing Demented Patients

1993 ◽  
Vol 5 (2) ◽  
pp. 117-134 ◽  
Author(s):  
Tsuyoshi Nishimura ◽  
Toshiko Kobayashi ◽  
Shiro Hariguchi ◽  
Masatoshi Takeda ◽  
Tomoko Fukunaga ◽  
...  
Keyword(s):  
Daily Living ◽  
Rating Scale ◽  
Comprehensive Evaluation ◽  
Daily Life ◽  
The Elderly ◽  
Mental States ◽  
Elderly Subjects ◽  
Daily Life Activities ◽  
Dementia Patients ◽  
Moderate Dementia

In the diagnosis, treatment, and care of dementia patients in the senile stage, comprehensive evaluation of ability in daily life and mental function is needed. Using a simple behavioral rating scale for the mental states (NM scale) and activities of daily living (N-ADL) of the elderly, we evaluated 250 elderly subjects. According to the NM scale, the scores for subjects in whom the severity was clinically diagnosed were as follows: normal, 50–48; borderline, 47–43; mild dementia, 42–31; moderate dementia, 30–17; and severe dementia, 16–0. Screening for dementia and determining its severity were readily accomplished using the NM scale, and basic activities in the daily life of the elderly could be evaluated effectively using the N-ADL. There was a significant correlation (r=0.863) between the Hasegawa dementia scale and the NM scale (p<0.001), a significant correlation (r=−0.947) between intellectual function scores of the GBS scale and the NM scale, and a significant correlation (r=0.944) between motor function score of the GBS scale and the N-ADL score. Evaluations of daily life activities can be made not only by psychiatrists and clinical psychologists, but by nonspecialists as well, because they are based on data obtained by observation of daily life behaviors; thus, assessment is appropriate both in clinical settings and in places of living.

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