Gait Analysis Using Smartwatches

Erdem, Naime Şeyma; Ersoy, Cem; Tunca, Can

doi:10.1109/pimrcw.2019.8880821

Cited by 29 publications

(25 citation statements)

References 15 publications

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“…In a study conducted by Joundi et al [ 7 ], a smartphone’s built-in accelerometer was used to evaluate the types of tremor, confirming that the fundamental frequencies of tremors can be compared with those measured using EMG analysis. More recent works use accelerometers based in smartwatches, as well as accelerometers in wrists, feet and legs [ 15 , 16 , 17 , 18 , 19 , 20 ]. From those references, the most similar to our work is Reference [ 15 ], but in this paper, the authors use Machine Learning techniques to analyze the data, and they do not make a clinical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…More recent works use accelerometers based in smartwatches, as well as accelerometers in wrists, feet and legs [ 15 , 16 , 17 , 18 , 19 , 20 ]. From those references, the most similar to our work is Reference [ 15 ], but in this paper, the authors use Machine Learning techniques to analyze the data, and they do not make a clinical analysis. Other interesting references to mention are References [ 21 , 22 , 23 ], which are systematic revisions of gait analysis using different wearable devices, like accelerometers but mainly inertial units.…”

Section: Introductionmentioning

confidence: 99%

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

Rincón

Valderrama

González

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rincón

Valderrama

González

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Some wearable devices are also connected with mobile phones through health applications. Various studies have been reported in the literature showing the use of these wearable devices ( Figure 6) and mobile computing in real-time monitoring [46][47][48][49]. Castillejo et al have proposed an activity recognition method by integrating wearable devices in a wireless sensor network for remote monitoring of patients through an e-health mobile application [50].…”

Section: Wearable Devicesmentioning

confidence: 99%

IoT-Based Applications in Healthcare Devices

Pradhan

Bhattacharyya

Pal

2021

Journal of Healthcare Engineering

260

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The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

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“…Combinations of these sensors are called IMU (Inertial Measurement Unit). An inertial sensor or IMU is used either stand-alone [10][11][12][13] or integrated into a smart device such as smartphone [14], smartwatch [15] or fitness tracker [16].…”

Section: State Of the Artmentioning

confidence: 99%

Assessing the Mobility of Elderly People in Domestic Smart Home Environments

Friedrich¹,

Steen²,

Fudickar³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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A continuous monitoring of the physical strength and mobility of elderly people is important for maintaining their health and treating diseases at an early stage. However, frequent screenings by physicians are exceeding the logistic capacities. An alternate approach is the automatic and unobtrusive collection of functional measures by ambient sensors. In the current publication, we show the correlation among data of ambient motion sensors and the wellestablished mobility assessment Short-Physical-Performance-Battery and Tinetti. We use the average number of motion sensor events for correlation with the assessment scores. The evaluation on a real-world dataset shows a moderate to strong correlation with the scores of standardised geriatrics physical assessments.

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Gait Analysis Using Smartwatches

Cited by 29 publications

References 15 publications

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

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

IoT-Based Applications in Healthcare Devices

Assessing the Mobility of Elderly People in Domestic Smart Home Environments

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