Early Access Objective: Parkinsonian gait is a defining feature of Shaking Palsy (SP) and it has one of the worse impact on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking.
The correct evaluation of the healing process is important to define proper times of fixator dynamization and removal, avoiding refractures. Unfortunately, a quantitative healing assessment is not yet available in clinical practice.
Objective: To demonstrate the utility of rheoencephalography (REG) for measuring cerebral blood flow and fluid dynamics during different stages of sleep. Methods: Anteroposterior cranial electrical impedance was measured with concurrent polysomnography in a group of healthy subjects during sleep.
Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time consuming for nurses and physicians.
A non-intrusive sleep apnea detection system using a C-Band channel sensing technique is proposed to monitor sleep apnea syndrome in real time. The system utilizes perturbations of RF signals to differentiate between patient’s breathing under normal and sleep apnea conditions.
Effective management of neurogenic orthostatic hypotension and supine hypertension (SH-OH) due autonomic failure requires a frequent and timely adjustment of medication throughout the day to maintain the blood pressure (BP) within the normal range – i.e., an accurate depiction of BP is a key prerequisite of effective management. One of the emerging technologies that provide one’s circadian and long-term physiological status with increased usability is unobtrusive zero-effort monitoring.
Part of the Special Issue Advanced Internet of Things in a Personalized Healthcare System: Validation, Analysis and Utilization The health of patients in the Intensive Care Unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed.
This paper presents a new platform to monitor the arm and hand activity of stroke patients during rehabilitation exercises in hospital and at home during their daily living activities. The platform provides relevant data to the therapist in order to assess the patients physical state and adapt the rehabilitation program if necessary.
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