Low-cost indoor health monitoring system

Moatamed, Babak; Arjun,; Shahmohammadi, Farhad; Ramezani, Ramin; Naeim, Arash; Sarrafzadeh, Majid

doi:10.1109/bsn.2016.7516252

Cited by 22 publications

(12 citation statements)

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“…According to the most recent census statistics, by 2050, the population aged 65 years and older is projected to double in size to 83.7 million in the United States [1]. With the increase of this geriatric population, health care utilization will increase dramatically, with a concomitant demand for rehabilitation and in-home care after hospitalization [2]. Finding the best way to support patients during rehabilitation, both at facilities and in home, without compromising patient safety is considered to be a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Finding the best way to support patients during rehabilitation, both at facilities and in home, without compromising patient safety is considered to be a significant challenge. The importance of patient safety and rehabilitation has highlighted the need for constant vigilance and fostered methodologies by which patients can be remotely monitored [2-8].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, tracking individuals’ personalized behavioral patterns may allow for the creation of actionable messages to patients and caregivers to improve patient health and outcomes [10]. The purpose of this study was to investigate the physical activity and indoor localization features obtained from our remote patient monitoring system, Sensing At-Risk Population (SARP) [2,11-14]. This study reports on SARP sensor–based markers for rehabilitation screening within a geriatric population, exploring if SARP can be used to prospectively distinguish between at-risk patients in a subacute rehabilitation environment.…”

Section: Introductionmentioning

confidence: 99%

“…Details of the system architecture with proximity-based sensors (beacons) and a Bluetooth-enabled smartwatch as its main components can be found in the study by Moatamed et al [2] and the patent application by Ramezani et al [14]. Building models for physical activity tracking and indoor localization was based on data collected using (1) commercially available Sony SmartWatch 3 with built-in EM7180 ± 2 g triaxial accelerometer, 420 mA battery, and BCM43340 Bluetooth module and (2) proximity beacons (MCU ARM Cortex-M4 32-bit processor with floating-point unit).…”

Section: Introductionmentioning

confidence: 99%

“…In studies by Bouchard et al [11-13], we proposed a few methods and considerations that can help enhance the indoor localization accuracies. A summary of the ground truth testing executed at the rehabilitation facility shown in Figure 1, with an overall accuracy >80%, can be found in a study by Moatamed et al [2].…”

Section: Introductionmentioning

confidence: 99%

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A Combination of Indoor Localization and Wearable Sensor–Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results

Ramezani¹,

Zhang²,

Xie³

et al. 2019

JMIR Mhealth Uhealth

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Background Health care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. Objective The study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting. Methods We analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features. Results Standing time percentage ( P <.001, d =1.51), laying down time percentage ( P <.001, d =1.35), resident room energy intensity ( P <.001, d =1.25), resident bed energy intensity ( P <.001, d =1.23), and energy percentage of active state ( P =.001, d =1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room ( P <.001, d =1.25) was achieved by capturing indoor localization information. Random forests revealed that...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%