People-Centric Service for mHealth of Wheelchair Users in Smart Cities

Yang, Lin; Li, Wenfeng; Ge, Yanhong; Fu, Xiuwen; Gravina, Raffaele; Fortino, Giancarlo

doi:10.1007/978-3-319-00491-4_9

Cited by 20 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Smartwatches are being used for patients with speech disorders to train their speech functions in remote settings [106], [93]. IoT systems are built in smart cities for improving the access for wheelchair users who face mobility related challenges in everyday life [107]. IoT also can help gather information from individuals about their special needs remotely in their comfortable environments [108].…”

Section: Iot For Individuals With Disabilities and Special Needsmentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

798

371

View full text Add to dashboard Cite

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: 1) device, 2) fog computing and 3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy.

show abstract

Section: Iot For Individuals With Disabilities and Special Needsmentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

798

371

View full text Add to dashboard Cite

show abstract

“…Localization-collected data are used in correlation with other health-related data in e-health platforms. In [23], a medical platform focused on people using wheelchairs is presented. The platform introduces new hardware approaches such as the smart wheelchair with localization and falling sense abilities.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Tegou

Kalamaras

Tsipouras

et al. 2019

Sensors

View full text Add to dashboard Cite

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).

show abstract

“…This approach mainly focuses on collecting targeted information about users' daily patterns and interactions [24,25]. Although the sensory coverage of spaces [21], events [19], health [22,23], and social interactions [24][25][26] is beneficial for people or citizens [20], people-centric sensing is an immature research area for detecting built environment system defects because most current people-centric sensory research has focused on humans or the interaction between humans, not on the interaction between users' responses and external systems. One representative method that estimates external system conditions using user responses is pothole detection on a motor vehicle road.…”

Section: People Centric Sensingmentioning

confidence: 99%

A people-centric sensing approach to detecting sidewalk defects

Kim

Yang

2016

Advanced Engineering Informatics

View full text Add to dashboard Cite

A defective sidewalk inhibits the walkability of a street and may also cause safety accidents (slips, trips, and falls) for pedestrians. When a pedestrian walks along a sidewalk, his/her behaviors may vary according to the condition of the sidewalk-e.g., whether the surface is normal, holed, cracked, tilted, or sloped. As a result, the pedestrian's stability may also change according to the built environment's conditions. Accordingly, this paper examines the feasibility of using pedestrians' physical behaviors to detect defects in a sidewalk. Pedestrians' physical responses and paths over a sidewalk are collected using an inertial measurement unit (IMU) sensor and a global positioning system (GPS). Then, after aggregating the pedestrians' bodily responses and locations, the irregularity of multiple pedestrians' responses are calculated in relation to their locations. The locations that show irregularities in the pedestrian-response patterns present a high correlation with the existence of a defect. The results of this study will help improve the continuous diagnosis of defects in sidewalks, thereby enhancing these built environment systems' serviceability.

show abstract

People-Centric Service for mHealth of Wheelchair Users in Smart Cities

Cited by 20 publications

References 18 publications

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

A people-centric sensing approach to detecting sidewalk defects

Contact Info

Product

Resources

About