A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults
2. Materials and Methods
2.1. Conceptual Model
- Using the IoT protocols, all the data information, such as localization and sensor data through embedded IoT devices in the user’s home environment, could be transmitted to the server and stored in the database. Caregivers or physicians can also override the reminder and subscribe to each particular topic if needed.
- The MQTT protocol is based on the concept of bridging, which is available in some current MQTT broker implementations (such as Mosquitto, HiveMQ, and CloudMQTT). It connects a broker A to another broker B as a standard client, subscribing to all or a subset of the topics transmitted by clients to B. Since our task is to obtain data from the embedded devices and send notifications to the user’s AR user interface, we chose the MQTT broker HiveMQ (hivemq.com). This is a free cloud broker, which allows IoT devices to be connected to the cloud. By creating functions for each variable, we were able to separate the variables into different MQTT topics.
- Once an event occurs in a particular home location, all the pertinent data values are checked on the server. Therefore, after the decision-making process on the cloud, data values will be updated. The caregiver and the older adults receive appropriate notifications.
- An appropriate AR message will be sent to the user when the user interacts with a particular object (for example, when the user is looking for their medication) or when the sensor and localization tag detect an event (for example, when the user enters a dangerous zone at home). These messages are defined based on the decision of the fuzzy inference and the output values. The end user puts on an indoor positioning tag to monitor the real-time location of the user and make easy interaction with the home objects. The real-time position and orientation data values also form part of the decision-making engine’s inputs. Moreover, user activity patterns can be generated and stored in the database for further analysis.
- The AR messages are sent to the user’s AR device while interacting with selected objects, or an event is detected from the sensor and anchors data. These messages are the output of the decision-making engine included in the service-based application.
2.2. Sample Scenarios
- The user may skip an activity, such as having a shower or eating breakfast, because they fail to recall it.
- The user often repeats activities because they do not remember what they already did, such as taking their medication.
- The user may forget to complete a task correctly or avoids a critical activity, such as turning off the stove or closing the main entrance.
2.3. System Architecture
2.4. Embedded Devices
2.5. Object-Based Decision-Making Process
2.5.1. Fuzzy Logic Implementation
2.5.2. Input/Output Variables
2.5.3. Fuzzy Rule Base
- Leaving home: If (rain status is Yes) and (distance from the main entrance is Near) and (heading angle is Small), then (image message is picture 3) and (voice message is audio 3).
- Cooking: If (distance from the refrigerator is Near) and (heading angle is Small), then (image message is picture 2) and (voice message is audio 2).
- Daily activity reminder: If (the plant’s humidity is Very dry), then (text message number is text 1).
- Daily activity reminder: If (distance from the TV is Near) and (heading angle is Small), then (image message is picture 6) and (voice message is audio 6).
- Cooking: If (time is Late Afternoon) and (distance from the oven is Near) and (heading angle is Small), then (image message is picture 4) and (voice message is audio 4).
- Medication reminder: If (time is Evening), then (image message is picture 5) and (voice message is audio 5).
- Alarm: If (flame sensor status is Yes), then (audio message is audio 7) and (relay status is Yes).
- Alarm: If (gas sensor status is Yes), then (text message is text 2).and (relay status is Yes).
- High Temperature Reminder: If (temperature sensor status is Hot), then (text message is text 3).
- Low Temperature Reminder: If (temperature sensor status is Cold), then (text message is text 4).
- Danger zone: If (distance from the fireplace is Near), then (voice message is audio 8).
- Danger zone: If (distance from the balcony is Near) and (heading angle is Small), then (image message is picture 9) and (voice message is audio 9).
2.6. User Indoor Location Identification
2.7. Augmented Reality Application
3. Results and Experimental Setup
3.1. Experiment A: Danger Zones and Reminders
3.2. Experiment B: Alerting Based on User’s Location and Sensors’ Data Values
3.3. Experiment C: Network Latency and System Response Time
- Safety: One of the main challenges for the caregivers is to keep the person with memory impairment monitored. Caregivers cannot continuously watch the person, so an assistive system can benefit this case. The person can enter dangerous areas (for example, the balcony), fall, or even leave their home. In our experimental condition (Experiment and Setup A), we simulated this scenario and assessed the usability of the system in detecting dangerous areas and sending reminders to the user. The caregiver can also monitor these events.
- Personal assistance: Psychologists indicate that the engagement of people with memory impairment in routine activities is essential. An assistive tool with some functionalities of a personal assistant could be beneficial, mainly because people with MCI have problems with short-term memory and recent events. Thus, in Experiment and Setup B, we evaluated the system’s performance in sending correct reminders or turning off proper actuators to help the user complete an ongoing task based on the activated fuzzy rule. The system should perform correctly according to the object-based decision-making algorithm.
- Quick response: When people with memory impairment forget the place of an object or cannot complete an activity, they should be reminded how to make a correct decision. Otherwise, the person might be anxious or disappointed. In this regard, the AAL system should send reminders and alerts in real time. Thus, in the last experiment (Experiment and Setup C), we evaluated the system’s response time in sending such a message.
- The wearable devices, for example, localization tags and AR glasses, are required to be lightweight, small, consume lower power, and produce less heat, leading to scalability problems. As interesting as AR may look, some people may not be comfortable with wearing head-mounted displays all day. To overcome this challenge, we suggested lightweight AR glasses as an interaction device for the end user because of its lightweight and minimal heads-up screen, so that each person with MCI could potentially use it.
- In any AR application, it is necessary to be synchronized in real time for giving the user precise information. The device requires high bandwidth and the fastest data communication to keep the real-world and virtual content in sync. In this regard, the network latency in our proposed model should be satisfactory, and data loss should not occur after the execution of a series of data transfers.
- In some cases, AR can violate the user’s privacy and start saving personal preferences and information. In the proposed model, the collected data can only be shared with physicians or caregivers to monitor memory impairment progression and treatment response. Finally, individuals with MCI can control personal data based on their impairment stage.
5. Conclusions and Future Works
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Parameter||Fuzzy Membership Functions||Data Type||Direction|
|Heading angle||Large, medium, and small||Linguistic||Input|
|Distance||Very far, far, near||Linguistic||Input|
|Time||Midnight night evening, late afternoon early afternoon morning, early morning||Linguistic||Input|
|Temperature||Very hot, hot, warm, mild, cool, cold, very cold||Linguistic||Input|
|Humidity||Very humid, humid, dry, very dry||Linguistic||Input|
|Rain detection||Yes, No||Boolean||Input|
|Flame detection||Yes, No||Boolean||Input|
|Gas detection||Yes, No||Boolean||Input|
|Relay status||Yes, No||Boolean||Output|
|Voice message||1, 2, …, 20||Integer||Output|
|Image message||1, 2, …, 20||Integer||Output|
|Text message||1, 2, …, 10||Integer||Output|
|Object Number||Experiment Number||Distance (dm)||Heading Angle (degree)||Membership Function of Distance||Membership Function of Heading Angle||Activated Rule Number|
|Experiment Number||User’s Location (x,y)||User’s Heading Angle (degree)||Location of|
|Location of an|
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Ghorbani, F.; Ahmadi, A.; Kia, M.; Rahman, Q.; Delrobaei, M. A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults. Sensors 2023, 23, 2673. https://doi.org/10.3390/s23052673
Ghorbani F, Ahmadi A, Kia M, Rahman Q, Delrobaei M. A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults. Sensors. 2023; 23(5):2673. https://doi.org/10.3390/s23052673Chicago/Turabian Style
Ghorbani, Fatemeh, Amirmasoud Ahmadi, Mohammad Kia, Quazi Rahman, and Mehdi Delrobaei. 2023. "A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults" Sensors 23, no. 5: 2673. https://doi.org/10.3390/s23052673