Feature Extraction in Sit-to-Stand Task Using M-IMU Sensors and Evaluatiton in Parkinson's Disease

Tosi, Jacopo; Summa, Susanna; Taffoni, Fabrizio; Biase, Lazzaro di; Marano, Massimo; Rizzo, Angelo Cascio; Tombini, Mario; Schena, Emiliano; Formica, Domenico; Pino, Giovanni Di

doi:10.1109/memea.2018.8438737

Cited by 12 publications

(10 citation statements)

References 29 publications

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“…In line with cardinal motor symptoms, to date, gait problems are evaluated with semiquantitative rating scales like the unified Parkinson's disease rating scale (UPDRS) [13] or the movement disorders society unified Parkinson's disease rating scale (MDS-UPDRS) [14]. In an effort to improve PD management and move towards a quantitative and home-oriented assessment and recognition of PD motor symptoms, different technologies have been used to evaluate bradykinesia [15][16][17], rigidity [17][18][19][20], tremor [21][22][23] and axial symptoms [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Biase

Santo

Caminiti

et al. 2020

Sensors

124

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The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.

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Section: Introductionmentioning

confidence: 99%

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Biase

Santo

Caminiti

et al. 2020

Sensors

124

View full text Add to dashboard Cite

show abstract

“…Systematic error accumulation reduces the accuracy and needs to be implemented with specific algorithms. Furthermore, these devices are bulky, with wired connections, and are uncomfortable for wearable applications [6]. Traditional inertial devices have good accuracy standards, but they are often uncomfortable, require high standards of compliance and suffer from systematic error accumulation.…”

Section: Discussionmentioning

confidence: 99%

“…Blumrosen, Uziel [162] also proposed a technology to assess tremor for the diagnosis of neurological pathologies and its monitoring. A feasibility test was conducted by examining the system performance against an arm model that fluctuated in the range of clinical tremor frequencies (3)(4)(5)(6)(7)(8)(9)(10)(11)(12). The UWB-based acquisition system showed a frequency estimation error of less than 0.1 Hz and provided a set of tremor amplitudes along the tremulous body part.…”

Section: Tremor Assessmentmentioning

confidence: 99%

“…Recent trends in the field of personalized medicine highlighted the necessity of empowering medicine with available telemonitoring technologies [ 1 ]. Good example are given by movement disorders like Parkinson’s disease [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ], dystonia [ 9 , 10 , 11 ] or tremor [ 12 , 13 , 14 , 15 ], in which telemonitoring solutions are a promising approach to improve the diagnostic and therapeutic process [ 11 , 16 ]. This approach has been further motivated by the upcoming requests of patients suffering from chronic disorders and their caregivers to receive customized therapies with high accuracy standards and at low costs, since indoor activities appear to be globally more demanding for older people for various reasons.…”

Section: Global Overview On Current Monitoring Systemsmentioning

confidence: 99%

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Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

Biase

Pecoraro

et al. 2022

Sensors

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Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients’ movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.

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“…Afterward, the signal was reconstructed by using the inverse continuous wavelet transform (icwt) between 10 Hz and 40 Hz in order to eliminate frequencies associated with slow-varying trends (e.g., respiratory activity) and isolate only the high-frequency packets representative of cardiac activity. The choice to use icwt was made because it allows for greater selectivity in the frequency band of interest, compared with classical bandpass filters, allowing noise cancellation to be performed without distortion of the raw signal [ 23 , 31 , 32 , 33 ]. Then, the root mean square envelope with a sliding window of 40 samples was applied to the reconstructed signal to emphasize each heartbeat.…”

Section: Methodsmentioning

confidence: 99%

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring

et al. 2022

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The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems’ performance, which can be useful to spread this approach in clinical settings.

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Feature Extraction in Sit-to-Stand Task Using M-IMU Sensors and Evaluatiton in Parkinson's Disease

Cited by 12 publications

References 29 publications

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring

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