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Wearable and Ambient Sensors and Devices for Elderly People

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4902

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Prof. Dr. Hugo E. Saner

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Guest Editor

ARTORG Center for Biomedical Engineering Research, Gerontechnology & Rehabilitation, University of Bern, Bern, Switzerland
Interests: digital biomarkers; independent living for elderly people; tele-rehabilitation

Special Issue Information

Dear Colleagues,

Sensor-based assessment of vital signs and symptoms has been increasingly used to facilitate care in times of limited resources and can be used in a hospital setting, in the homes of patients at risk for various diseases, and in the homes of elderly people. Digital biomarkers for health and disease can be extracted based on the sensor’s reading. Digital biomarkers can be used to drive patient-specific interventions or as information for formal and informal caregivers to help them optimize patient care. There are three categories of sensors that can be used in a patient's home or in the hospital: Object sensors (also called object-attached sensors) are connected to objects the patient interacts with in their daily life, measuring the interaction between the patient and the object. Wearable sensors are typically worn on the wrist of the non-dominant arm and are usually integrated into smart watches. Although wearable sensors do have their advantages, they are difficult to use in certain patient populations, such as the elderly, especially if cognitive impairment is present as cooperation of the patient is needed. Contact-free sensors (also called ambient sensors) are positioned in the environment of the patient and can measure activities via behavior recognition algorithms and physiological parameters without the active involvement of the user.

The aim of this Special Issue of Sensors is to assemble a coherent set of papers that contribute to the understanding of how to optimally use and integrate sensor-based assessment of vital signs, symptoms, and behavior into existing care systems.

Prof. Dr. Hugo E. Saner
Guest Editor

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Keywords

wearable sensors
ambient sensors
elderly care
independent living
vital signs monitoring

Published Papers (4 papers)

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Research

19 pages, 3995 KiB

Open AccessArticle

A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments

by Michael Single, Lena C. Bruhin, Aaron Colombo, Kevin Möri, Stephan M. Gerber, Jacob Lahr, Paul Krack, Stefan Klöppel, René M. Müri, Urs P. Mosimann and Tobias Nef

Sensors 2024, 24(4), 1172; https://doi.org/10.3390/s24041172 - 10 Feb 2024

Viewed by 653

Abstract

Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual’s gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (

R^{2} > 0.9

) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems. Full article

(This article belongs to the Special Issue Wearable and Ambient Sensors and Devices for Elderly People)

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22 pages, 4735 KiB

Open AccessArticle

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers

by Rajaram Narasimhan, Muthukumaran Gopalan, Mohamed Yacin Sikkandar, Ahmad Alassaf, Ibrahim AlMohimeed, Khalid Alhussaini, Adham Aleid and Sabarunisha Begum Sheik

Sensors 2023, 23(21), 8867; https://doi.org/10.3390/s23218867 - 31 Oct 2023

Viewed by 1089

Abstract

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer’s disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual’s activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults’ activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful. Full article

(This article belongs to the Special Issue Wearable and Ambient Sensors and Devices for Elderly People)

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13 pages, 1485 KiB

Open AccessArticle

Ecological Fall Prediction Sensitivity, Specificity, and Accuracy in Patients with Mild Cognitive Impairment at a High Risk of Falls

by Chaesu Kim, Haeun Park and Joshua (Sung) You

Sensors 2023, 23(15), 6977; https://doi.org/10.3390/s23156977 - 6 Aug 2023

Cited by 1 | Viewed by 1133

Abstract

While falls among patients with mild cognitive impairment (MCI) have been closely associated with an increased postural sway during ecological activities of daily living, there is a dearth of postural sway detection (PSD) research in ecological environments. The present study aimed to investigate the fall sensitivity, specificity, and accuracy of our PSD system. Forty healthy young and older adults with MCI at a high risk of falls underwent the sensitivity, specificity, and accuracy tests for PSD by simultaneously recording the Berg Balance Scale and Timed Up and Go in ecological environments, and the data were analyzed using the receiver operating characteristic curve and area under the curve. The fall prediction sensitivity ranged from 0.82 to 0.99, specificity ranged from 0.69 to 0.90, and accuracy ranged from 0.53 to 0.81. The PSD system’s fall prediction sensitivity, specificity, and accuracy data suggest a reasonable discriminative capacity for distinguishing between fallers and non-fallers as well as predicting falls in older adults with MCI in ecological testing environments. Full article

(This article belongs to the Special Issue Wearable and Ambient Sensors and Devices for Elderly People)

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14 pages, 1850 KiB

Open AccessArticle

Association of 7-Day Profiles of Motor Activity in Marital Dyads with One Component Affected by Parkinson’s Disease

by Marco Rabuffetti, Ennio De Giovannini, Ilaria Carpinella, Tiziana Lencioni, Luca Fornia and Maurizio Ferrarin

Sensors 2023, 23(3), 1087; https://doi.org/10.3390/s23031087 - 17 Jan 2023

Viewed by 1207

Abstract

(1) Background: A noticeable association between the motor activity (MA) profiles of persons living together has been found in previous studies. Social actigraphy methods have shown that this association, in marital dyads composed of healthy individuals, is greater than that of a single person compared to itself. This study aims at verifying the association of MA profiles in dyads where one component is affected by Parkinson’s disease (PD). (2) Methods: Using a wearable sensor-based social actigraphy approach, we continuously monitored, for 7 days, the activities of 27 marital dyads including one component with PD. (3) Results: The association of motor activity profiles within a marital dyad (cross-correlation coefficient 0.344) is comparable to the association of any participant with themselves (0.325). However, when considering the disease severity quantified by the UPDRS III score, it turns out that the less severe the symptoms, the more associated are the MA profiles. (4) Conclusions: Our findings suggest that PD treatment could be improved by leveraging the MA of the healthy spouse, thus promoting lifestyles also beneficial for the component affected by PD. The actigraphy approach provided valuable information on habitual functions and motor fluctuations, and could be useful in investigating the response to treatment. Full article

(This article belongs to the Special Issue Wearable and Ambient Sensors and Devices for Elderly People)

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