Ambient Technology to Assist Elderly People in Indoor Risks
2.1. Search Items
2.2. Inclusion/Exclusion Criteria
2.3. Data Extraction and Analysis
3. Major Indoor Risks Affecting Elderly People and the Assistive Technology
3.1. Fall Risk
3.1.1. Ambient Assistive Technology for Indoor Fall Risk
3.2. Risk of Wrong Self-Medication Management (Non-Adherence, Abuse and Misuse)
3.2.1. Ambient Assistive Technology for Wrong Self-Medication Management Risk
3.3. Risks of Fire, Burns and Intoxication by Gas/Smoke
3.3.1. Risk of Fire
3.3.2. Risk of Burns
3.3.3. Risk of Intoxication by Gas/Smoke
3.3.4. Ambient Assistive Technology for Fire Risk
3.3.5. Ambient Assistive Technology for Burn Risk
3.3.6. Ambient Assistive Technology for Intoxication by Gas/Smoke Risk
3.4. Risk of Inactivity and the Assistive Technology
4. Conclusions and Future Directions
Conflicts of Interest
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|Indoor Risk||# Reviewed Papers||# Selected Relevant Papers|
|Wrong self-medication management (non-adherence, abuse and misuse)||123||73|
|Fire, burns and intoxication by gas/smoke||84||32|
|Wearable Devices||Non-Wearable Devices|
|Sufficient training data||Fixed threshold||||Vision-based sensors/one class classification to distinguish fall from other activities.|||
|Sequential classification (HMM)||[50,51]||Channel State Information (CSI) of WiFi signals||[56,57]|
|Insufficient training data||Fall as abnormal activity||||Acoustic sensors/one class SVM|||
|System||Monitoring and Detection of Medication Adherence||Interventions|
|SmartPillBox ||Pill count/weight.||Reminds patients to take the prescribed medications, and contacts caregivers through telephone to store the information of the patient’s compliance.|
|Electronic pillboxes, MedTracker ||Opening lid of a sub-compartment of the pillbox.||Not found in the paper.|
|Medication Event Monitoring Systems (MEMS) ||Associated sensing and recording devices. Displays information about the date and time of opening the vial.||It can be programmed to alert patients to take medication. A medication event monitoring system operates through the Internet, interconnecting and accommodating the transfer of information and data between a patient, a caregiver, and a pharmacist.|
|Ingestible sensor ||Ingestible sensor microchips embedded in medication transmit a signal when the medication is ingested or even metabolized.||The sensor communicates with the monitor that is worn on the user’s torso. The information stored in the monitor is sent wirelessly using Bluetooth to a mobile phone.|
|Magic Medicine Cabinet ||RFID to identify which medications were taken out of a cabinet, face recognition to identify who approached the device.||Reminds patients to take medication.|
|Medication management ||RFID- and wireless sensors–based medication taking monitoring; situation awareness and decision-making. The inference of reminding time is based on fuzzy logic.||Reminds an elderly person to take medications in a predicted time based on learning person’s habits and regular meal-taking schedule.|
|Web-based medicine intake tracking application ||RFID readers and tags, motion sensors, and a wireless sensor mote.||A Web-based caregiver module.|
|Automated medication dispensers [93,94,95,96], HealthWatch, Beep N Tel ||Dispense medication at preprogrammed intervals.||Voice-mail reminders, video-telephone reminders, automated telephone calls. Reminders are both audible (spoken words and tones) and visual (a flashing red strobe light). Caregiver/family member notification.|
|MoviPill ||Persuasive technology.||A mobile phone–based game that persuades elderly patients to be more adherent to their medication prescription by means of social competition.|
|Fire/Smoke Alarms||Fire Signature||Sound Alert|
|Existing commercial cooking devices: e.g., StoveGuard, SafeCook and HomeSensor||Programmable cooking modes. Temperature of the oven surface.||Integrate LEDs to indicate that an oven surface is hot. |
Switch off an oven if there is no attendance after certain programmed time.
|Lushaka et al. ||Existing smoke alarms to detect a potential fire risk.||Switching off oven power supply.|
|Doman et al. ||Reminds user to follow the correct steps when performing a cooking task through audio and video. No reaction if a risk occurs.|
|Yahui et al. ||Visual surveillance system with multiple cameras enables to observe cooking conditions, and track user activities.||Not completely automatic, since it requires observer intervention (caregiver).|
|Sanchez et al. ||Detects rapid variations in temperature and smoke in the kitchen.||Sends a notification (with camera shots) to the fire department and caregivers. In addition, the system activates exhaust fans and a fire extinguishing suppression system.|
|Alwan et al.  |
Wai et al. 
|Measure oven usage. |
Detect unsafe usage of the oven, the levels of abnormality in the kitchen.
Both systems use embedded temperature sensors to measure the burner status, ultrasonic sensors to detect the presence of a pot and electric current sensors to detect the oven usage.
|Switches off power.|
|Yuan et al. ||Thermal camera to detect dangerous situation.||Alerts user or caregiver when a dangerous situation occurs.|
|Yared et al. [107,108,151]||Preventive approach. Fire detection based on measurements of the concentration of the volatile organic compounds (VOC) and alcohol in the cooking smoke using appropriate selected VOC and alcohol sensors. Fire risk is determined by fuzzy logic reasoning.||Alerts user ubiquitously through audio and visual notification that a risk situation occurs. The interventions depend on the severity level of the detected risk.|
|Chen et al., Hagen et al., Johnson, Milke et al., [143,144,145,146,147]||Intelligent techniques and methods are used to fuse the data obtained by diverse sensors that lead to determine the fire probability. Multi-sensor data fusion detection technology based on fuzzy logic . Fire detection by monitoring: carbon monoxide (CO), carbon dioxide (CO2), or volatile organic compounds (VOC).||No interventions.|
|Bashyal et al.  |
Charumporn et al. 
|Classify fire situations according to their triggering reasons by monitoring: VOC, humidity, and ambient temperature. Fire detection algorithm based on neural networks.||No interventions.|
|Physical/Social Activities Promoting Representative Examples|
|Video games||PiNiZoRo |
|Exergaming||Social exergames to persuade seniors to increase physical activity |
|Motivation||QueFaire , Flowie , Playful |
|Communication between people (virtual or real)||Virtual imaginary interlocutor |
Promoting Intergenerational communication through location-based asynchronous video communication 
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Yared, R.; Abdulrazak, B. Ambient Technology to Assist Elderly People in Indoor Risks. Computers 2016, 5, 22. https://doi.org/10.3390/computers5040022
Yared R, Abdulrazak B. Ambient Technology to Assist Elderly People in Indoor Risks. Computers. 2016; 5(4):22. https://doi.org/10.3390/computers5040022Chicago/Turabian Style
Yared, Rami, and Bessam Abdulrazak. 2016. "Ambient Technology to Assist Elderly People in Indoor Risks" Computers 5, no. 4: 22. https://doi.org/10.3390/computers5040022