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Editorial

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5 pages, 195 KiB

Open AccessEditorial

Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research

by Carl-Philipp Jansen, Katharina Gordt-Oesterwind and Michael Schwenk

Sensors 2022, 22(3), 973; https://doi.org/10.3390/s22030973 - 27 Jan 2022

Cited by 3 | Viewed by 2864

Abstract

Wearable motion sensors have been gaining ground for quite some time now; a large proportion of research projects in the field of physical activity, health, and mobility are being carried out using an electronic form of outcome assessment, and there are good reasons [...] Read more.

Wearable motion sensors have been gaining ground for quite some time now; a large proportion of research projects in the field of physical activity, health, and mobility are being carried out using an electronic form of outcome assessment, and there are good reasons for this [...] Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

Research

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17 pages, 667 KiB

Open AccessArticle

Specific Motor and Cognitive Performances Predict Falls during Ward-Based Geriatric Rehabilitation in Patients with Dementia

by Klaus Hauer, Ilona Dutzi, Katharina Gordt and Michael Schwenk

Sensors 2020, 20(18), 5385; https://doi.org/10.3390/s20185385 - 20 Sep 2020

Cited by 10 | Viewed by 3308

Abstract

The aim of this study was to identify in-hospital fall risk factors specific for multimorbid hospitalized geriatric patients with dementia (PwD) during hospitalization. Geriatric inpatients during ward-based rehabilitation (n = 102; 79.4% females; 82.82 (6.19) years of age; 20.26 (5.53) days of [...] Read more.

The aim of this study was to identify in-hospital fall risk factors specific for multimorbid hospitalized geriatric patients with dementia (PwD) during hospitalization. Geriatric inpatients during ward-based rehabilitation (n = 102; 79.4% females; 82.82 (6.19) years of age; 20.26 (5.53) days of stay) were included in a comprehensive fall risk assessment combining established clinical measures, comprehensive cognitive testing including detailed cognitive sub-performances, and various instrumented motor capacity measures as well as prospective fall registration. A combination of unpaired t-tests, Mann–Whitney-U tests, and Chi-square tests between patients with (“in-hospital fallers”) and without an in-hospital fall (“in-hospital non-fallers”), univariate and multivariate regression analysis were used to explore the best set of independent correlates and to evaluate their predictive power. In-hospital fallers (n = 19; 18.63%) showed significantly lower verbal fluency and higher postural sway (p < 0.01 to 0.05). While established clinical measures failed in discriminative as well as predictive validity, specific cognitive sub-performances (verbal fluency, constructional praxis, p = 0.01 to 0.05) as well as specific instrumented balance parameters (sway area, sway path, and medio-lateral displacement, p < 0.01 to 0.03) significantly discriminated between fallers and non-fallers. Medio-lateral displacement and visuospatial ability were identified in multivariate regression as predictors of in-hospital falls and an index combining both variables yielded an accuracy of 85.1% for fall prediction. Results suggest that specific cognitive sub-performances and instrumented balance parameters show good discriminative validity and were specifically sensitive to predict falls during hospitalization in a multimorbid patient group with dementia and an overall high risk of falling. A sensitive clinical fall risk assessment strategy developed for this specific target group should include an index of selected balance parameters and specific variables of cognitive sub-performances. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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13 pages, 237 KiB

Open AccessArticle

What Features and Functions Are Desired in Telemedical Services Targeted at Polish Older Adults Delivered by Wearable Medical Devices?—Pre-COVID-19 Flashback

by Eliasz Kańtoch and Anna Kańtoch

Sensors 2020, 20(18), 5181; https://doi.org/10.3390/s20185181 - 11 Sep 2020

Cited by 11 | Viewed by 2943

Abstract

The emerging wearable medical devices open up new opportunities for the provision of health services and promise to accelerate the development of novel telemedical services. The main objective of this study was to investigate the desirable features and applications of telemedical services for [...] Read more.

The emerging wearable medical devices open up new opportunities for the provision of health services and promise to accelerate the development of novel telemedical services. The main objective of this study was to investigate the desirable features and applications of telemedical services for the Polish older adults delivered by wearable medical devices. The questionnaire study was conducted among 146 adult volunteers in two cohorts (C.1: <65 years vs. C.2: ≥65 years). The analysis was based on qualitative research and descriptive statistics. Comparisons were performed by Pearson’s chi-squared test. The questionnaire, which was divided into three parts (1-socio-demographic data, needs, and behaviors; 2-health status; 3-telemedicine service awareness and device concept study), consisted of 37 open, semi-open, or closed questions. Two cohorts were analyzed (C.1: n = 77; mean age = 32 vs. C.2: n = 69; mean age = 74). The performed survey showed that the majority of respondents were unaware of the telemedical services (56.8%). A total of 62.3% of C.1 and 34.8% of C.2 declared their understanding of telemedical services. The 10.3% of correct explanations regarding telemedical service were found among all study participants. The most desirable feature was the detection of life-threatening and health-threatening situations (65.2% vs. 66.2%). The findings suggest a lack of awareness of telemedical services and the opportunities offered by wearable telemedical devices. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

18 pages, 2175 KiB

Open AccessArticle

Concurrent Validity, Test-Retest Reliability, and Sensitivity to Change of a Single Body-Fixed Sensor for Gait Analysis during Rollator-Assisted Walking in Acute Geriatric Patients

by Christian Werner, Patrick Heldmann, Saskia Hummel, Laura Bauknecht, Jürgen M. Bauer and Klaus Hauer

Sensors 2020, 20(17), 4866; https://doi.org/10.3390/s20174866 - 28 Aug 2020

Cited by 12 | Viewed by 3684

Abstract

Body-fixed sensor (BFS) technology offers portable, low-cost and easy-to-use alternatives to laboratory-bound equipment for analyzing an individual’s gait. Psychometric properties of single BFS systems for gait analysis in older adults who require a rollator for walking are, however, unknown. The study’s aim was [...] Read more.

Body-fixed sensor (BFS) technology offers portable, low-cost and easy-to-use alternatives to laboratory-bound equipment for analyzing an individual’s gait. Psychometric properties of single BFS systems for gait analysis in older adults who require a rollator for walking are, however, unknown. The study’s aim was to evaluate the concurrent validity, test-retest-reliability, and sensitivity to change of a BFS (DynaPort MoveTest; McRoberts B.V., The Hague, The Netherlands) for measuring gait parameters during rollator-assisted walking. Fifty-eight acutely hospitalized older patients equipped with the BFS at the lower back completed a 10 m walkway using a rollator. Concurrent validity was assessed against the Mobility Lab (APDM Inc.; Portland, OR, USA), test-retest reliability over two trials within a 15 min period, and sensitivity to change in patients with improved, stable and worsened 4 m usual gait speed over hospital stay. Bland–Altman plots and intraclass correlation coefficients (ICC) for gait speed, cadence, step length, step time, and walk ratio indicate good to excellent agreement between the BFS and the Mobility Lab (ICC_2,1 = 0.87–0.99) and the repeated trials (ICC_2,1 = 0.83–0.92). Moderate to large standardized response means were observed in improved (gait speed, cadence, step length, walk ratio: 0.62–0.99) and worsened patients (gait speed, cadence, step time: −0.52 to −0.85), while those in stable patients were trivial to small (all gait parameters: −0.04–0.40). The BFS appears to be a valid, reliable and sensitive instrument for measuring spatio-temporal gait parameters during rollator-assisted walking in geriatric patients. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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14 pages, 829 KiB

Open AccessArticle

Activity Energy Expenditure Predicts Clinical Average Levels of Physical Activity in Older Population: Results from Salus in Apulia Study

by Ilaria Bortone, Fabio Castellana, Luisa Lampignano, Roberta Zupo, Biagio Moretti, Gianluigi Giannelli, Francesco Panza and Rodolfo Sardone

Sensors 2020, 20(16), 4585; https://doi.org/10.3390/s20164585 - 15 Aug 2020

Cited by 11 | Viewed by 2570

Abstract

Self-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. Resolving the differences between self-reported and objectively measured physical activity is an important surveillance challenge currently facing population health experts. The present work aims [...] Read more.

Self-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. Resolving the differences between self-reported and objectively measured physical activity is an important surveillance challenge currently facing population health experts. The present work aims at providing the relationship between activity energy expenditure estimated from wrist-worn accelerometers and intensity of self-reported physical activity (InCHIANTI structured interview questionnaire) in a sub-cohort of a population-based study on aging in Southern Italy. Linear regression was used to test the association between measured and reported physical activity. We found that activity energy expenditure predicted clinical average levels of PA assessed through InCHIANTI classification. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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14 pages, 3579 KiB

Open AccessArticle

Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson’s Disease

by Domiciano Rincón, Jaime Valderrama, Maria Camila González, Beatriz Muñoz, Jorge Orozco, Linda Montilla, Yor Castaño and Andrés Navarro

Sensors 2020, 20(15), 4339; https://doi.org/10.3390/s20154339 - 4 Aug 2020

Cited by 10 | Viewed by 4610

Abstract

In patients with Parkinson’s disease (PD), arm swing changes are common, even in the early stages, and these changes are usually evaluated subjectively by an expert. In this article, hypothesize that arm swing changes can be detected using a low-cost, cloud-based, wearable, sensor [...] Read more.

In patients with Parkinson’s disease (PD), arm swing changes are common, even in the early stages, and these changes are usually evaluated subjectively by an expert. In this article, hypothesize that arm swing changes can be detected using a low-cost, cloud-based, wearable, sensor system that incorporates triaxial accelerometers. The aim of this work is to develop a low-cost, assistive diagnostic tool for use in quantifying the arm swing kinematics of patients with PD. Ten patients with PD and 11 age-matched, healthy subjects are included in the study. Four feature extraction techniques were applied: (i) Asymmetry estimation based on root mean square (RMS) differences between arm movements; (ii) posterior–anterior phase and cycle regularity through autocorrelation; (iii) tremor energy, established using Fourier transform analysis; and (iv) signal complexity through the fractal dimension by wavelet analysis. The PD group showed significant (p < 0.05) reductions in arm swing RMS values, higher arm swing asymmetry, higher anterior–posterior phase regularities, greater “high energy frequency” signals, and higher complexity in their XZ plane signals. Therefore, the novel, portable system provides a reliable means to support clinical practice in PD assessment. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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10 pages, 497 KiB

Open AccessArticle

Quantifying Habitual Physical Activity and Sedentariness in Older Adults—Different Outcomes of Two Simultaneously Body-Worn Motion Sensor Approaches and a Self-Estimation

by Rieke Trumpf, Wiebren Zijlstra, Peter Haussermann and Tim Fleiner

Sensors 2020, 20(7), 1877; https://doi.org/10.3390/s20071877 - 28 Mar 2020

Cited by 9 | Viewed by 2799

Abstract

Applicable and accurate assessment methods are required for a clinically relevant quantification of habitual physical activity (PA) levels and sedentariness in older adults. The aim of this study is to compare habitual PA and sedentariness, as assessed with (1) a wrist-worn actigraph, (2) [...] Read more.

Applicable and accurate assessment methods are required for a clinically relevant quantification of habitual physical activity (PA) levels and sedentariness in older adults. The aim of this study is to compare habitual PA and sedentariness, as assessed with (1) a wrist-worn actigraph, (2) a hybrid motion sensor attached to the lower back, and (3) a self-estimation based on a questionnaire. Over the course of one week, PA of 58 community-dwelling subjectively healthy older adults was recorded. The results indicate that actigraphy overestimates the PA levels in older adults, whereas sedentariness is underestimated when compared to the hybrid motion sensor approach. Significantly longer durations (hh:mm/day) for all PA intensities were assessed with the actigraph (light: 04:19; moderate to vigorous: 05:08) when compared to the durations (hh:mm/day) that were assessed with the hybrid motion sensor (light: 01:24; moderate to vigorous: 02:21) and the self-estimated durations (hh:mm/day) (light: 02:33; moderate to vigorous: 03:04). Actigraphy-assessed durations of sedentariness (14:32 hh:mm/day) were significantly shorter when compared to the durations assessed with the hybrid motion sensor (20:15 hh:mm/day). Self-estimated duration of light intensity was significantly shorter when compared to the results of the hybrid motion sensor. The results of the present study highlight the importance of an accurate quantification of habitual PA levels and sedentariness in older adults. The use of hybrid motion sensors can offer important insights into the PA levels and PA types (e.g., sitting, lying) and it can increase the knowledge about mobility-related PA and patterns of sedentariness, while actigraphy appears to be not recommendable for this purpose. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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13 pages, 6118 KiB

Open AccessArticle

Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags

by Haneul Jung, Bummo Koo, Jongman Kim, Taehee Kim, Yejin Nam and Youngho Kim

Sensors 2020, 20(5), 1277; https://doi.org/10.3390/s20051277 - 26 Feb 2020

Cited by 26 | Viewed by 4562

Abstract

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the [...] Read more.

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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10 pages, 615 KiB

Open AccessArticle

Change of Objectively-Measured Physical Activity during Geriatric Rehabilitation

by Jochen Klenk, Sebastian Wekenmann, Lars Schwickert, Ulrich Lindemann, Clemens Becker and Kilian Rapp

Sensors 2019, 19(24), 5451; https://doi.org/10.3390/s19245451 - 11 Dec 2019

Cited by 10 | Viewed by 2949

Abstract

This prospective study investigated feasibility and sensitivity of sensor-based physical activity (PA) measures to monitor changes in PA during geriatric rehabilitation and its relation to clinical parameters at admission. PA was routinely measured at day 2 and day 15 after admission in 647 [...] Read more.

This prospective study investigated feasibility and sensitivity of sensor-based physical activity (PA) measures to monitor changes in PA during geriatric rehabilitation and its relation to clinical parameters at admission. PA was routinely measured at day 2 and day 15 after admission in 647 patients (70.2% women, mean age = 82.0 (SD = 7.19) years) of a German geriatric hospital using a thigh-worn accelerometer. Clinical records were used to include age, Barthel Index, diagnosis, mobility, orientation and cognition. Mean values and 95% confidence intervals (95%-CI) of walking duration, walking bout duration and number of sit-to-stand transfers were calculated to quantify different domains of PA. All observed PA parameters improved during rehabilitation, regardless of age, diagnosis or physical and cognitive function at admission. Walking duration increased by 12.1 (95%-CI: 10.3; 13.8) min, walking bout duration by 2.39 (95%-CI: 1.77; 3.00) s, and number of sit-to-stand transfers by 7 (95%-CI: 5; 8). Floor and ceiling effects were not observed. Walking duration at day 2 as well as day 15 was continuously associated with Barthel Index and statistically significant improved for all levels of Barthel Index. In summary, this study showed that sensor-based PA monitoring is feasible to assess the individual progress in geriatric rehabilitation patients. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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15 pages, 3487 KiB

Open AccessArticle

Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

by Wouter Bijnens, Jos Aarts, An Stevens, Darcy Ummels and Kenneth Meijer

Sensors 2019, 19(24), 5344; https://doi.org/10.3390/s19245344 - 4 Dec 2019

Cited by 14 | Viewed by 3632

Abstract

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that [...] Read more.

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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20 pages, 3924 KiB

Open AccessArticle

Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features

by Wolfgang Teufl, Bertram Taetz, Markus Miezal, Michael Lorenz, Juliane Pietschmann, Thomas Jöllenbeck, Michael Fröhlich and Gabriele Bleser

Sensors 2019, 19(22), 5006; https://doi.org/10.3390/s19225006 - 16 Nov 2019

Cited by 53 | Viewed by 4523

Abstract

Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles [...] Read more.

Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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30 pages, 6881 KiB

Open AccessArticle

Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

by Michelle Pasquale Fillekes, Eun-Kyeong Kim, Rieke Trumpf, Wiebren Zijlstra, Eleftheria Giannouli and Robert Weibel

Sensors 2019, 19(20), 4551; https://doi.org/10.3390/s19204551 - 19 Oct 2019

Cited by 26 | Viewed by 6326

Abstract

Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived [...] Read more.

Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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22 pages, 856 KiB

Open AccessArticle

Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

by Diana Gomes, João Mendes-Moreira, Inês Sousa and Joana Silva

Sensors 2019, 19(12), 2803; https://doi.org/10.3390/s19122803 - 22 Jun 2019

Cited by 10 | Viewed by 3734

Abstract

The increasingly aging society in developed countries has raised attention to the role of technology in seniors’ lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition [...] Read more.

The increasingly aging society in developed countries has raised attention to the role of technology in seniors’ lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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24 pages, 1508 KiB

Open AccessArticle

An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment

by Alice Coni, Sabato Mellone, Marco Colpo, Jack M. Guralnik, Kushang V. Patel, Stefania Bandinelli and Lorenzo Chiari

Sensors 2019, 19(10), 2227; https://doi.org/10.3390/s19102227 - 14 May 2019

Cited by 7 | Viewed by 4365

Abstract

Physical capability (PC) is conventionally evaluated through performance-based clinical assessments. We aimed to transform a battery of sensor-based functional tests into a clinically applicable assessment tool. We used Exploratory Factor Analysis (EFA) to uncover the underlying latent structure within sensor-based measures obtained in [...] Read more.

Physical capability (PC) is conventionally evaluated through performance-based clinical assessments. We aimed to transform a battery of sensor-based functional tests into a clinically applicable assessment tool. We used Exploratory Factor Analysis (EFA) to uncover the underlying latent structure within sensor-based measures obtained in a population-based study. Three hundred four community-dwelling older adults (163 females, 80.9 ± 6.4 years), underwent three functional tests (Quiet Stand, QS, 7-meter Walk, 7MW and Chair Stand, CST) wearing a smartphone at the lower back. Instrumented tests provided 73 sensor-based measures, out of which EFA identified a fifteen-factor model. A priori knowledge and the associations with health-related measures supported the functional interpretation and construct validity analysis of the factors, and provided the basis for developing a conceptual model of PC. For example, the “Walking Impairment” domain obtained from the 7MW test was significantly associated with measures of leg muscle power, gait speed, and overall lower extremity function. To the best of our knowledge, this is the first time that a battery of functional tests, instrumented through a smartphone, is used for outlining a sensor-based conceptual model, which could be suitable for assessing PC in older adults and tracking its changes over time. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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Review

Jump to: Editorial, Research, Other

16 pages, 2627 KiB

Open AccessReview

Characterizing Behavioral Activity Rhythms in Older Adults Using Actigraphy

by Ariel B. Neikrug, Ivy Y. Chen, Jake R. Palmer, Susan M. McCurry, Michael Von Korff, Michael Perlis and Michael V. Vitiello

Sensors 2020, 20(2), 549; https://doi.org/10.3390/s20020549 - 19 Jan 2020

Cited by 30 | Viewed by 5674

Abstract

Wrist actigraphy has been used to assess sleep in older adult populations for nearly half a century. Over the years, the continuous raw activity data derived from actigraphy has been used for the characterization of factors beyond sleep/wake such as physical activity patterns [...] Read more.

Wrist actigraphy has been used to assess sleep in older adult populations for nearly half a century. Over the years, the continuous raw activity data derived from actigraphy has been used for the characterization of factors beyond sleep/wake such as physical activity patterns and circadian rhythms. Behavioral activity rhythms (BAR) are useful to describe individual daily behavioral patterns beyond sleep and wake, which represent important and meaningful clinical outcomes. This paper reviews common rhythmometric approaches and summarizes the available data from the use of these different approaches in older adult populations. We further consider a new approach developed in our laboratory designed to provide graphical characterization of BAR for the observed behavioral phenomenon of activity patterns across time. We illustrate the application of this new approach using actigraphy data collected from a well-characterized sample of older adults (age 60+) with osteoarthritis (OA) pain and insomnia. Generalized additive models (GAM) were implemented to fit smoothed nonlinear curves to log-transformed aggregated actigraphy-derived activity measurements. This approach demonstrated an overall strong model fit (R² = 0.82, SD = 0.09) and was able to provide meaningful outcome measures allowing for graphical and parameterized characterization of the observed activity patterns within this sample. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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24 pages, 679 KiB

Open AccessReview

Analyzing the Use of Accelerometers as a Method of Early Diagnosis of Alterations in Balance in Elderly People: A Systematic Review

by Raquel Leirós-Rodríguez, Jose L. García-Soidán and Vicente Romo-Pérez

Sensors 2019, 19(18), 3883; https://doi.org/10.3390/s19183883 - 9 Sep 2019

Cited by 50 | Viewed by 5459

Abstract

Alterations of balance are a growing public health problem as they affect one in three adults over the age of 65, and one in two over the age of 80. Identifying the factors that affect postural stability is essential in designing specific interventions [...] Read more.

Alterations of balance are a growing public health problem as they affect one in three adults over the age of 65, and one in two over the age of 80. Identifying the factors that affect postural stability is essential in designing specific interventions to maintain the independence and mobility of older people. The aim of this review was to understand the use of accelerometers in order to assess the balance in older people. Analyzing the most appropriate evaluation methodology and protocolizing it will optimize the processes of early identification of balance alterations. However, quantitative assessment methods of balance are usually limited to a laboratory environment, a factor that can be overcome by accelerometers. A systematic search was carried out across eight databases where accelerometers were employed to assess balance in older people. Articles were excluded if they focused on sensor design and did not measure balance or apply the technology on targeted participants. A total of 19 articles were included for full-text analysis, where participants took part in the balance evaluation monitored by accelerometers. The analysis of spatio-temporal parameters and the magnitude of the accelerations recorded by the devices were the most common study variables. Accelerometer usage has potential to positively influence interventions based on physical exercise to improve balance and prevent falls in older people. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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18 pages, 746 KiB

Open AccessReview

Can We Rely on Mobile Devices and Other Gadgets to Assess the Postural Balance of Healthy Individuals? A Systematic Review

by Alexandre S. Pinho, Ana P. Salazar, Ewald M. Hennig, Barbara C. Spessato, Antoinette Domingo and Aline S. Pagnussat

Sensors 2019, 19(13), 2972; https://doi.org/10.3390/s19132972 - 5 Jul 2019

Cited by 20 | Viewed by 4206

Abstract

The consequences of falls, costs, and complexity of conventional evaluation protocols have motivated researchers to develop more effective balance assessments tools. Healthcare practitioners are incorporating the use of mobile phones and other gadgets (smartphones and tablets) to enhance accessibility in balance evaluations with [...] Read more.

The consequences of falls, costs, and complexity of conventional evaluation protocols have motivated researchers to develop more effective balance assessments tools. Healthcare practitioners are incorporating the use of mobile phones and other gadgets (smartphones and tablets) to enhance accessibility in balance evaluations with reasonable sensitivity and good cost–benefit. The prospects are evident, as well as the need to identify weakness and highlight the strengths of the different approaches. In order to verify if mobile devices and other gadgets are able to assess balance, four electronic databases were searched from their inception to February 2019. Studies reporting the use of inertial sensors on mobile and other gadgets to assess balance in healthy adults, compared to other evaluation methods were included. The quality of the nine studies selected was assessed and the current protocols often used were summarized. Most studies did not provide enough information about their assessment protocols, limiting the reproducibility and the reliability of the results. Data gathered from the studies did not allow us to conclude if mobile devices and other gadgets have discriminatory power (accuracy) to assess postural balance. Although the approach is promising, the overall quality of the available studies is low to moderate. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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Open AccessLetter

Association between Daily-Life Gait Quality Characteristics and Physiological Fall Risk in Older People

by Sabine Schootemeijer, Roel H.A. Weijer, Marco J.M. Hoozemans, Kimberley S. van Schooten, Kim Delbaere and Mirjam Pijnappels

Sensors 2020, 20(19), 5580; https://doi.org/10.3390/s20195580 - 29 Sep 2020

Cited by 4 | Viewed by 3230

Abstract

Gait quality characteristics obtained from accelerometry during daily life are predictive of falls in older people but it is unclear how they relate to fall risk. Our aim was to test whether these gait quality characteristics are associated with the severity of fall [...] Read more.

Gait quality characteristics obtained from accelerometry during daily life are predictive of falls in older people but it is unclear how they relate to fall risk. Our aim was to test whether these gait quality characteristics are associated with the severity of fall risk. We collected one week of trunk accelerometry data from 279 older people (aged 65–95 years; 69.5% female). We used linear regression to investigate the association between six daily-life gait quality characteristics and categorized physiological fall risk (QuickScreen). Logarithmic rate of divergence in the vertical (VT) and anteroposterior (AP) direction were significantly associated with the level of fall risk after correction for walking speed (both p < 0.01). Sample entropy in VT and the mediolateral direction and the gait quality composite were not significantly associated with the level of fall risk. We found significant differences between the high fall risk group and the very low- and low-risk groups, the moderate- and very low-risk and the moderate and low-risk groups for logarithmic rate of divergence in VT and AP (all p ≤ 0.01). We conclude that logarithmic rate of divergence in VT and AP are associated with fall risk, making them feasible to assess the physiological fall risk in older people. Full article

(This article belongs to the Special Issue Wearable Motion Sensors Applied in Older Adults)

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