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Wearable Sensors for Gait and Falls Monitoring

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

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 41295

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Special Issue Editors

Prof. Dr. Michael Vassallo

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Guest Editor

Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust and Bournemouth University, United Kingdom
Interests: falls; cognitive impairment; rehabilitation

Prof. Dr. Hongnian Yu

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Guest Editor

School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5QT, UK
Interests: robotics and intelligent control; applied artificial intelligence; data analysis; data sciences with applications in digital healthcare and manufacturing systems; applications of emerging technology, such as RFID, wireless technology, etc. into healthcare and manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yanhong Liu

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Guest Editor

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: rehabilitation robot; human-robot interaction and collaboration

Dr. Arif Reza Anwary

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Guest Editor

Swansea University Medical School, Swansea University, Swansea, UK
Interests: gait analysis; wearable technology and machine learning

Special Issue Information

Dear Colleagues,

Falls and any resulting injuries and disabilities remain major public health concerns worldwide. The Global Burden of Diseases, Injuries and Risk Factors Study 2017 ranked falls as the 18th leading cause of age-standardized rates of disability worldwide. Gait analysis and falls go hand in hand, as abnormalities in the former often lead to the latter. Over the years there have been considerable advancements in health assessment technology using sensors coupled to the rise of big data. Such advances continue to drive important research on new applications for this technology to support the management of this major societal healthcare challenge. Yet more needs to be done. The ongoing COVID-19 pandemic and the resultant need for self-isolation and social distancing have presented new challenges to the delivery of effective healthcare remotely. Such challenges also create new opportunities. There is a need to develop newer and better technologies that can support digital transformation to be able to assess gait, predict and detect falls, reduce injury and facilitate the remote delivery of healthcare in newer and innovative ways and to reduce health inequity. This Special Issue of Sensors aims to promote and support leading research in this area.

Prof. Dr. Michael Vassallo
Prof. Dr. Hongnian Yu
Prof. Dr. Yanhong Liu
Dr. Arif Reza Anwary
Guest Editors

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Keywords

Falls
Digital transformation
Big data
Balance capability analysis
Gait control
Wearable Technology
Movement Analysis
Machine Learning
Rehabilitation
Mobile/Remote Monitoring
Body area/sensor Networks
Pervasive Health Technologies
Home Healthcare

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Published Papers (11 papers)

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Research

24 pages, 7937 KiB

Open AccessArticle

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents

by Amanrai Singh Kahlon, Khushboo Verma, Alexander Sage, Samuel C. K. Lee and Ahad Behboodi

Sensors 2023, 23(19), 8275; https://doi.org/10.3390/s23198275 - 6 Oct 2023

Viewed by 2008

Abstract

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8–18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior–inferior shank acceleration (SI shank Acc) and medial–lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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12 pages, 5295 KiB

Open AccessArticle

Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method

by Shaobing Wang and Jiang Wu

Sensors 2023, 23(14), 6360; https://doi.org/10.3390/s23146360 - 13 Jul 2023

Cited by 6 | Viewed by 2395

Abstract

Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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17 pages, 1030 KiB

Open AccessArticle

An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors

by Zabir Mohammad, Arif Reza Anwary, Muhammad Firoz Mridha, Md Sakib Hossain Shovon and Michael Vassallo

Sensors 2023, 23(10), 4774; https://doi.org/10.3390/s23104774 - 15 May 2023

Cited by 9 | Viewed by 3954

Abstract

Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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20 pages, 1339 KiB

Open AccessArticle

Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

by Hsiao-Lung Chan, Yuan Ouyang, Rou-Shayn Chen, Yen-Hung Lai, Cheng-Chung Kuo, Guo-Sheng Liao, Wen-Yen Hsu and Ya-Ju Chang

Sensors 2023, 23(1), 495; https://doi.org/10.3390/s23010495 - 2 Jan 2023

Cited by 9 | Viewed by 2661

Abstract

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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21 pages, 2191 KiB

Open AccessArticle

A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems

by Pablo Aqueveque, Britam Gómez, Patricia A. H. Williams and Zheng Li

Sensors 2022, 22(13), 4677; https://doi.org/10.3390/s22134677 - 21 Jun 2022

Cited by 1 | Viewed by 1822

Abstract

Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient’s motor control. Lately, technological systems for human motion analysis have been used in clinics to identify abnormal movement states, while they generally suffer from privacy challenges and concerns especially at home or in remote places. This paper presents a novel privacy preservation and quantification methodology that imitates the forgetting process of human memory to protect privacy in patient-centric healthcare. The privacy preservation principle of this methodology is to change the traditional data analytic routines into a distributed and disposable form (i.e., DnD) so as to naturally minimise the disclosure of patients’ health data. To help judge the efficacy of DnD-based privacy preservation, the researchers further developed a risk-driven privacy quantification framework to supplement the existing privacy quantification techniques. To facilitate validating the methodology, this research also involves a home-care-oriented movement analysis system that comprises a single inertial measurement sensor and a mobile application. The system can acquire personal information, raw data of movements and indexes to evaluate the risk of falls and gait at homes. Moreover, the researchers conducted a technological appreciation survey of 16 health professionals to help understand the perception of this research. The survey obtains positive feedback regarding the movement analysis system and the proposed methodology as suitable for home-care scenarios. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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14 pages, 4872 KiB

Open AccessArticle

Ground Contact Time Estimating Wearable Sensor to Measure Spatio-Temporal Aspects of Gait

by Severin Bernhart, Stefan Kranzinger, Alexander Berger and Gerfried Peternell

Sensors 2022, 22(9), 3132; https://doi.org/10.3390/s22093132 - 20 Apr 2022

Cited by 3 | Viewed by 3240

Abstract

Inpatient gait analysis is an essential part of rehabilitation for foot amputees and includes the ground contact time (GCT) difference of both legs as an essential component. Doctors communicate improvement advice to patients regarding their gait pattern based on a few steps taken at the doctor’s visit. A wearable sensor system, called Suralis, consisting of an inertial measurement unit (IMU) and a pressure measuring sock, including algorithms calculating GCT, is presented. Two data acquisitions were conducted to implement and validate initial contact (IC) and toe-off (TO) event detection algorithms as the basis for the GCT difference determination for able-bodied and prosthesis wearers. The results of the algorithms show a median GCT error of −51.7 ms (IMU) and 14.7 ms (sensor sock) compared to the ground truth and thus represent a suitable possibility for wearable gait analysis. The wearable system presented, therefore, enables a continuous feedback system for patients and, above all, a remote diagnosis of spatio-temporal aspects of gait behaviour based on reliable data collected in everyday life. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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12 pages, 307 KiB

Open AccessArticle

The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors

by Jaime Martín-Martín, Li Wang, Irene De-Torres, Adrian Escriche-Escuder, Manuel González-Sánchez, Antonio Muro-Culebras, Cristina Roldán-Jiménez, María Ruiz-Muñoz, Fermín Mayoral-Cleries, Attila Biró, Wen Tang, Borjanka Nikolova, Alfredo Salvatore and Antonio I. Cuesta-Vargas

Sensors 2022, 22(7), 2552; https://doi.org/10.3390/s22072552 - 26 Mar 2022

Cited by 4 | Viewed by 3227

Abstract

Through this study, we developed and validated a system for energy expenditure calculation, which only requires low-cost inertial sensors and open source R software. Five healthy subjects ran at ten different speeds while their kinematic variables were recorded on the thigh and wrist. Two ActiGraph wireless inertial sensors and a low-cost Bluetooth-based inertial sensor (Lis2DH12), assembled by SensorID, were used. Ten energy expenditure equations were automatically calculated in a developed open source R software (our own creation). A correlation analysis was used to compare the results of the energy expenditure equations. A high interclass correlation coefficient of estimated energy expenditure on the thigh and wrist was observed with an Actigraph and Sensor ID accelerometer; the corrected Freedson equation showed the highest values, and the Santos-Lozano vector magnitude equation and Sasaki equation demonstrated the lowest one. Energy expenditure was compared between the wrist and thigh and showed low correlation values. Despite the positive results obtained, it was necessary to design specific equations for the estimation of energy expenditure measured with inertial sensors on the thigh. The use of the same formula equation in two different placements did not report a positive interclass correlation coefficient. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

12 pages, 2855 KiB

Open AccessArticle

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

by Vytautas Bucinskas, Andrius Dzedzickis, Juste Rozene, Jurga Subaciute-Zemaitiene, Igoris Satkauskas, Valentinas Uvarovas, Rokas Bobina and Inga Morkvenaite-Vilkonciene

Sensors 2021, 21(15), 5240; https://doi.org/10.3390/s21155240 - 3 Aug 2021

Cited by 28 | Viewed by 8006

Abstract

Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat^®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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13 pages, 1492 KiB

Open AccessArticle

Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

by Jeremiah Hauth, Safa Jabri, Fahad Kamran, Eyoel W. Feleke, Kaleab Nigusie, Lauro V. Ojeda, Shirley Handelzalts, Linda Nyquist, Neil B. Alexander, Xun Huan, Jenna Wiens and Kathleen H. Sienko

Sensors 2021, 21(14), 4661; https://doi.org/10.3390/s21144661 - 7 Jul 2021

Cited by 8 | Viewed by 4525

Abstract

Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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16 pages, 3492 KiB

Open AccessArticle

Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2

by María del Carmen Miranda-Duro, Laura Nieto-Riveiro, Patricia Concheiro-Moscoso, Betania Groba, Thais Pousada, Nereida Canosa and Javier Pereira

Sensors 2021, 21(10), 3341; https://doi.org/10.3390/s21103341 - 11 May 2021

Cited by 7 | Viewed by 4358

Abstract

Background: Presently the use of technological devices such as wearable devices has emerged. Physical activity monitoring with wearable sensors is an easy and non-intrusive approach to encourage preventive care for older adults. It may be useful to follow a continuous assessment of the risk of falling. The objective is to explore the relationship between the daily activity measured by Xiaomi Mi Band 2 and the risk of falling of older adults residing in or attending care facilities. Methods: A cross-sectional study was conducted on three different institutions located in Galicia (autonomous community) (Spain). Results: A total of 31 older adults were included in the study, with a mean age of 84 ± 8.71 years old. The main findings obtained were that a greater number of steps and distance could be related to a lower probability of falling, of dependency in basic activities of daily living, or of mobility problems. Conclusions: The importance of focusing on daily steps, intrinsically related to the objective assessment of daily physical activity, is that it is a modifiable factor that impacts different aspects of health and quality of life. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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17 pages, 25394 KiB

Open AccessArticle

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

by Chia-Yeh Hsieh, Hsiang-Yun Huang, Kai-Chun Liu, Chien-Pin Liu, Chia-Tai Chan and Steen Jun-Ping Hsu

Sensors 2021, 21(9), 3302; https://doi.org/10.3390/s21093302 - 10 May 2021

Cited by 4 | Viewed by 2782

Abstract

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)

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