Posture monitoring in healthcare: a systematic mapping study and taxonomy

Camboim, Bruno Dahmer; da Rosa Tavares, João Elison; Tavares, Mauricio Campelo; Barbosa, Jorge Luis Victória

doi:10.1007/s11517-023-02851-w

Cited by 5 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As postural diagnosis is based in two steps, monitoring and anomaly detection, it is of interest to make an analysis of the existing devices based on the postural data collection system and another analysis dedicated to sitting posture anomaly detection techniques. While in [ 37 ] an analysis of monitoring devices and in [ 38 ] a compilation of postural classification techniques is carried out, no work presents a complete analysis of both devices and techniques that explore in depth into the problem of detecting sitting posture anomalies, that is, the whole diagnosis systems are not reviewed. Furthermore, the focus of both articles is distant from wheelchair users, who, as will be discussed later, exhibit distinctive characteristics that must be taken into account when developing such systems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome

Gautam,

Gupta,

Shrivastava

2024

Eng. Res. Express

View full text Add to dashboard Cite

Carpal tunnel is associated with long-term use of the wrist and hand for various activities such as typing, welding, or poor working postures. Carpal Tunnel Syndrome (CTS) may cause severe pain and discomfort in the hand and wrist, and in some circumstances, surgery becomes inevitable. The objective of this study is to prevent typing postures, which can be ascertained as predisposing subjects to CTS development. The data used in this study is an array of wrist wearable sensors to capture flexion, extension, and bending of fingers while using a keyboard or mouse.In this research, conducted an exploratory data analysis (EDA) to gain an initial understanding of the dataset. Following the exploratory phase and applied feature extraction techniques, specifically Principal Component Analysis (PCA). As put forward for the proposed research, the strategies to prevent risky occupations have broad potential at the present time, especially in the case of CTS when preventing repetitive wrist movements. Machine learning is employed on the data in order to identify risk factors indicative of a high probability of CTS. The analyzed models are linear regression, Support Vector Machine, Random Forest, Multilayer Perceptron, Convolution Neural Network, and Long Short Term Memory. The conditions for assessing the performance of the data models include RMS error, coefficients of determination, and mean absolute percentage error.

show abstract

Using Ultra-Short-Term Heart Rate Variability (HRV) Analysis to Track Posture Changes

Mohammadpoor Faskhodi,

Fernandez-Chimeno,

Garcia-Gonzalez

2024

IEEE Access

View full text Add to dashboard Cite

Posture monitoring in healthcare: a systematic mapping study and taxonomy

Cited by 5 publications

References 52 publications

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome

Using Ultra-Short-Term Heart Rate Variability (HRV) Analysis to Track Posture Changes

Contact Info

Product

Resources

About