Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges
Abstract
:1. Motivation
2. Introduction
3. Remote Health Monitoring
3.1. E-Health and M-Health
3.2. Home-Based Remote Health Monitoring
4. Internet-of-Things and Connected Homes
4.1. Layered Architechture of Smart Home
4.1.1. Sensors and Actuators
4.1.2. Communication Network
4.1.3. Computing and Decision Making Platform
4.1.4. Services
4.2. Interoperability and Standardization
5. Smart Monitoring Systems for Elderly and People with Disability
5.1. Automated Emergency Call Systems
5.2. Automated Activity and Fall Detection Systems
5.3. Vital Signs Monitoring Systems
5.4. Reminding Systems
5.5. Automated Health Assessment
6. Smart Homes for Elderly Healthcare: Prototypes and Commercial Solutions
6.1. Smart Home Solutions in the Literature
6.2. Commercial Solutions for Remote Elderly Care
7. Research Challenges for Smart Homes
8. Future Perspectives and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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WBAN Applications | Wearable WBAN |
|
Implantable WBAN |
| |
Remote Health Monitoring |
Wireless Tech. | Frequency | Range | Data Rate | Power (mW) | Maximum Nodes | Network Topologies | Security |
---|---|---|---|---|---|---|---|
RFID | 13.56 MHz 860–960 MHz | 0–3 m | 640 kbps | 200 | 1 at a time | peer-to-peer (P2P) passive | N/A |
Bluetooth | 2.4–2.5 GHz | 1–100 m | 1–3 Mbps | 2.5–100 | 1 M + 7 S | P2P, star | 56–128 bit key |
BLE | 2.4–2.5 GHz | 1–100 m | 1 Mbps | 10 | 1 M + 7 S | P2P, star | 128-bit AES |
HomePlug GP | 1.8–30 MHz | ~100 m | 4–10 Mbps | 500 | - | P2P, star, tree and mesh | 128-bit AES |
EnOcean | 902, 928, 868 MHz | 30–300 m | 125 kbps | ~0.05 with energy harvesting | - | P2P, star, tree and mesh | 128-bit AES |
ZigBee | 2.4–2.5 GHz | 10–100 m | 250 kbps | 50 | 65,533 | P2P, star, tree and mesh | 128-bit AES |
WiFi | 2.4–2.5 GHz | 150–200 m | 54 Mbps | 1000 | 255 | P2P, star | WEP,WPA, WPA2 |
DASH7 | 315–915 MHz | 200 m–2 km | 167 kbps | <1 | - | P2P, star, tree and mesh | 128-bit AES |
Insteon | RF: 869.85, 915, 921 MHz powerline: 131.65 KHz | 40–50 m | 38 kbps (RF) 2–13 kbps (powerline) | - | 64,000 nodes per network | P2P, star, tree and mesh | 256-bit AES |
Sigfox | 868/902 MHz | 10–50 km | 10–1000 bps | 0.01–100 | - | P2P, star | No default encryption |
NFC | 13.56 MHz | 5 cm | 424 kbps | 15 | 1 at a time | P2P | AES |
Wireless HART™ | 2.4 GHz | 50–100 m | 10 | - | P2P, star, tree and mesh | 128-bit AES | |
6LoWPAN | 2.4 GHz | 25–50 m | 250 kbps | 2.23 | - | P2P, star, tree and mesh | 128-bit AES |
ANT | 2.4–2.5 GHz | 30 m | 20–60 kbps | 0.01–1 | 65,533 in one channel | P2P, star, tree and mesh | 64-bit key |
Z-Wave | 860–960 MHz | 100 m | 9.6–100 kbps | 100 | 232 | mesh | 128-bit AES |
Ref. | Proposition | Country (Year) | Resident activity Monitoring | Home Environment Monitoring | Resident Health Monitoring | Home Appliance Monitoring | Wireless Connectivity | Summary | Alert/Reminder Service |
---|---|---|---|---|---|---|---|---|---|
[116] | Fall detection system for smart home | China and Korea (2014) |
| Temperature and humidity sensors | Pulse pressure sensor: HR | ZigBee with multiple access points | Proposed but not implemented | ||
[133] | Daily activity tracking for smart home | Korea (2012) | RFID Tags and self-developed biosensor and logging system | RFID | Developed applications in android platform for tracking ADL of the elderly | Smartphone based application for the elderly and the caregivers, family | |||
[153] | Smart home based on cloud computing | Canada (2013) |
| Temperature, humidity, ambient light | Light and fans | ZigBee, RFID | Arduino-based application communicates with the user, sensors, and actuators as well as interacts with the cloud-based computing service | ||
[154] | Mobile healthcare system for wheelchair users | China and Canada (2014) |
| Temperature, humidity, smoke sensor |
| Lights and air conditions | ZigBee and Bluetooth | User can interact with the home environment remotely and locally via smart phones | Connected to a third-party service to notify emergency situation using SMS and telephone |
[155] | Cloud-based platform for assessing elderly health and wellbeing | USA (2004 to date) |
| Air quality and room temperature | Weight, heart rate, and body mass index | Bluetooth, WiFi, Zigbee | Developed a cloud-based cognitive and physical health assessment platform using mostly commercial ambient and passive sensing technologies. | ||
[156] | Smart home for elderly care | India (2015) |
| ZigBee | Developed an Arduino based software |
| |||
[157] | Sensor platform for healthcare services in a home environment | Bristol, UK (2016) |
| Temperature, humidity, luminosity, noise level, air quality, occupancy | Electricity metering, cold and hot water consumption | BLE , IEEE 80 2.15.4, WiFi |
| Remote system generates the alerts | |
[158] | Home tele-monitoring of vital parameters and detection of anomalies in daily activities | Milano, Italy (2017) |
| ECG , BP and SpO2 weight, ear temperature, glycaemia | Water tap, refrigerator, and dishwasher | Bluetooth LE | Developed a clinical governance system to interact between patient and clinicians | Clinical governance system generates and displays alerts both to the patient and the clinicians | |
[175] | Cloud-based home healthcare | USA and China (2016) |
| ECG, SpO2 | ZigBee | Cloud based service for storage, processing and interaction with healthcare personnel | |||
[176] | Activity and physiological parameter monitoring and social interaction | Sweden, Italy, Spain (2014) |
|
|
| Not mentioned | A telepresence robot with camera, lens, microphone and LCD screen to facilitate video communication with caregivers | Context recognition for event detection, trend analysis and alert generation | |
[177] | Monitoring and interactive robotic system | Spain (2015) |
|
| Alarms are programmed for fall, intrusion and abnormal pattern detection | ||||
[178] | Behavior and wellness prediction | New Zealand (2013) |
| Microwave, water kettle, toaster, room heater, TV | ZigBee | Software for data acquisition, activity recognition, behavior recognition and wellness determination are developed in C# | |||
[179] | Easy-to-install and lightweight smart home kit | USA (2013) | Infrared motion/light sensor, relays, Door sensor and temperature sensor | ZigBee | Developed a activity visualizer software to display and keep record of the ADL | ||||
[180] | In-home Health Monitoring System | Japan (2015) | IR motion sensor, water flow sensor for monitoring urination, kitchen work, and washing | RFID | Monitors and assesses occupant’s health status by monitoring urination, kitchen work, washing activities and movements in the house | Generates and e-mail report on the occupants’ health condition | |||
[181] | Health monitoring using data fusion techniques | France (2011) |
| Temperature sensors | Wearable device: HR | ZigBee |
| ||
Data fusion based on fuzzy logic: detect several distress situations |
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Majumder, S.; Aghayi, E.; Noferesti, M.; Memarzadeh-Tehran, H.; Mondal, T.; Pang, Z.; Deen, M.J. Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges. Sensors 2017, 17, 2496. https://doi.org/10.3390/s17112496
Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ. Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges. Sensors. 2017; 17(11):2496. https://doi.org/10.3390/s17112496
Chicago/Turabian StyleMajumder, Sumit, Emad. Aghayi, Moein Noferesti, Hamidreza Memarzadeh-Tehran, Tapas Mondal, Zhibo Pang, and M. Jamal Deen. 2017. "Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges" Sensors 17, no. 11: 2496. https://doi.org/10.3390/s17112496
APA StyleMajumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., & Deen, M. J. (2017). Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges. Sensors, 17(11), 2496. https://doi.org/10.3390/s17112496