- Age-related cognitive impairment is generally characterized by gradual memory loss and decision- making difficulties. The aim of this study is to investigate multi -level support and suggest relevant helping means for the elderly with mild cognitive impairment as well as their caregivers as the primary end -users. This work reports preliminary results on an intelligent assistive system, achieved through the integration of Internet of Things, augmented reality, and adaptive fuzzy decision -making methods. The proposed system operates in different modes, including automated and semi -automated modes. The former helps the user complete their daily life activities by showing augmented reality messages or making automatic changes; while the latter allo ws manual changes after the real -time assessment of the user’s cognitive state based on the augmented reality serious game score. We have also evaluated the accuracy of the serious game score with 37 elderly participants and compared it with users’ paper -based cognitive test results. We further noted that there is an acce ptable correlation between the paper -based test and users’ serious game scores. Moreover, we observed that the system response in the semi -automated mode causes less data loss compared with the automated mode, as the number of active devices decreases. Keywords: Augmented reality, Fuzzy decision -making, intelligent assistive technology, Internet of things, serious game. 1. Introduction Recent projections of the world population indicate obvious trends tending towards more elderly people with the proportion of the global population aged 65 years or over is expected to increase from 9.3 percent in 2020 to 16.0 percent by 2050 [1] . Moreover, age -related cognitive impairment is a degenerative neurological disorder charac - terized by progressive loss of memory affecting the elderly [2]. Loss of memory and lacking capacity to make decisions are two main difficulties experienced by the elderly and patients with cognitive impairments. Nearly 5.8 million patients are currently suffering from Alzheimer’s disease (AD), as the common cause of cognitive impairments, in the United States alone [3] . They generally have problems in remembering recent information and completing everyday tasks. Therefore, they should always be reminded of the required tasks that improve their confidence and quality of life [4]. In response to the increasing number of elder people with mild cognitive impairment (MCI) and a lack of treatment that slows or stops its progression, the pervasive depl oyment of intelligent assistive (IA) systems could have a disruptive effect on the elderly and their care- givers’ daily life [5]. IA systems can (i) reduce the burden on public finances throughout the postponement or removal of institutional care, (ii) lessen the psychological burden on formal caregivers and families, (iii) compensate for the lack of human caregivers while improving and optimizing the quality of care, and (iv) empower older adults with MCI and thereby enhancing their confidence [6] . For example, in [7] , the authors proposed MED -AR system based on the intelligent augmented reality (AR) for helping older adults in the medication task. The system was designed to present a research methodology for tracking and distributing prescribed medicines for older adults in a home health care scenario. On the other hand, serious games have had positive impacts on reconstructing a functional environment where the user could poten - tially en counter real-world scenarios [8–10]. Moreover, researchers 2 have shown that it is a valid evaluation tool for activities of daily living [11] as well as cognitive screening [12]. For instance, researchers used to evaluate activities of daily living through a serious game platform by integrating game playing with a serious purpose. The aim of the study was to assess functional independence in older people through five tasks using the Smart Aging game [13]. In another study, the authors devel - oped a serious game based on virtual reality to estimate cognitive dis - abilities during navigational tasks. Users had to follow arrows to get to special places and find the way back home on their own [14] . Even though the number of researches in this area has been significantly expanding, few researchers have addressed the development of IA sys - tems with possible application into age-related MCI care based on the fact that users’ daily cognitive abilities could be changed. The aim of our study is to suggest an IA system for elderly people with MCI, and improve their ability to complete everyday tasks on their own. In our previous study [15] , we implemented a task prompting system based on AR messages without any adaptive decision -making engine to evaluate the user’s general cognitive condition. To increase the system’s intelligence for making a correct reminder, we take advantage of an AR-based serious game assessment tool. The suggested AR -based serious game in this work is potentially capable of providing entertainment and mental state examination simultaneously. Our target population and primary end -users are in - dividuals experiencing MCI, who can understand the technology they are using and its expected assistance and risks. This preliminary study is focused on assessing the proposed system’s usability by the older adults (with and without MCI) and considering their feedback. In more detail, based upon the above suggestions, this paper pro - poses a system the following features: To implement the Internet of Things (IoT) system, the system bene - fits from the Message Queuing Telemetry Transport (MQTT) con - nectivity protocol for communication. All data values, such as indoor positioning data, embedded sensors, and cognitive serious game scores, can be published to the server in the system’s automatic mode. Family or caregivers can also subscribe to each particular topic and override the message for the user to send reminders if needed. In the automatic mode, once an event happens in a specific indoor location, all the relevant fuzzy rules are checked on the server. Thus, following the decision -making algorithm, data values are updated. Both the user and the caregiver could receive appropriate notifications. To monitor the user’s real-time location, the user wears an indoor localization tag. The localization tag sends the positioning informa - tion to the monitor module, and the monitor module receives all the published data. According to the AR serious game score, the performing mode of the IA system changes to the manual mode by real-time assessment of the user’s cognitive state. This approach leads to saving the devices’ energy, enhancing accuracy and performance. 2. Materials and methods 2.1. System architecture As the daily routine of an elderly with MCI is contingent on their surrounding objects in the environment, environment conditions such as temperature, humidity, and CO 2 level is essential for designing a smart home environment. In our work, such data is published on the cloud layer by placing different sensors and actuators in several locations such as kitchen, bedroom and washing room. Furthermore, Wireless Sensor Networks (WSNs) have been deployed in the local fog layer to collect data about the user’s location. Three anchors are placed in the home environment to estimate the user’s real- time location. All the communication to our IA system is done over MQTT with data serialized in JavaScript Object Notation format (JSON). To avoid un - necessary transmission of the data, the performing mode of the IA sys - tem can be changed to the semi -automated mode based on the decision - making algorithm results. We propose our model based on the localization tag worn by the user for positioning system. The application for making interaction with the end-user and assessing their cognitive function s is developed on an Android operating system, implemented on a smartphone. In an attempt to send all the data to the cloud, we consider MQTT protocol as the messaging protocol. MQTT is a commonly used appli- cation layer protocol to transmit data between the devices in IoT ar - chitecture due to its simplicity and scalability [16] . We have utilized HiveMQ server as a MQTT message broker and Wemos D1 Mini modules for each sensor, actuator, and data coordinator in the positioning system to collect user’s indoor data. Fig. 1 shows the general architecture of the proposed IA system in which the IA system works in its automated mode. We have also designed a service- based application (based on the C# cloud service) for caregivers to present the data related to the events detected by the sensors and to control the actuators, embedded in the smart home environment [17]. The user cognitive functions state and game res ults can also be monitored by the caregivers. 2.2. Adaptive decision- making process In this section, we consider an adaptive fuzzy method to convert our system into an intelligent and context -aware system that promotes monitoring of users and improving their ability to perform their daily tasks. To interact with the user correctly, the decision -making process should be adaptive and precise to avoid false notifications or false- positive AR messages. In some situations, such as completing daily tasks, people occasionally require assistance for memory recall. In order to update each variable of the IA system on the cloud and making the right decision, we employed an adaptive fuzzy logic model that selects the most proper values. The model of the fuzzy adaptive decision - making is shown in Fig. 2 . A fuzzy controller has four main components: rule base, inference system, fuzzifier interface, and defuzzifier interface. In our system, we have designed two knowledge bases elicited from the user’s cognitive state and the AR game score. These rule- bases are necessary for deciding when to provide aids and predict the user’s target accurately. Now we go through each part of them in our model. 2.2.1 . Variables and membership functions In our proposed model, the input variables are several types of data from embedded devices, the user’s real- time location, and their cogni- tive state. The output variables are different types of AR messages which are received by the user or their caregivers. Table 1 shows input and output parameters, data types, and their membership functions in our work. We can add other home objects and calculate the distance as a fuzzy input variable. By considering each input and output variable, mem - bership functions are defined based on the data types and fuzzy rules. Fig. 3 shows the membership function for the user’s movement time as well as the AR game score. Output variables are voice, image, and text messages , defined as fuzzy singleton membership functions. Each mes - sage has an identification number (ID) and can be activated by particular inputs. A triangular membership function describes the states of the relay actuators. 2.2.2 . Fuzzy rule -bases After defining eac h variable’s membership function, we must build the rule-base, including all of the expert IF-THEN rules. In our IA system, two main rule- bases are essential to creating an adaptive decision - making process and multi -level support. The first one helps the user complete the activities independently in their daily life by showing AR • • • • 3 Device Layer User Interface Patient Expert Device layer (sensors and actuators) Fig. 1. Intelligent Assistive system architecture. All IoT data values, such as indoor positioning data, embedded sensors, and serious game scores, are published to the server. Following the fuzzy decision -making algorithm and serious game score, data values are updated in the automatic mode. Fig. 2. Fuzzy adaptive decision -making model. Two knowledge bases are obtained from the user’s cognitive state and the serious game score. User and caregiver receive appropriate notifications based on the fuzzy rules’ outputs. Table 1 Description of Inputs and outputs of the fuzzy system. Parameter Fuzzy membership function Data type Game score Low, high Linguistic Game Start, Stop Boolean Once an event occurs (for example, medication time alarm), input data values such as location, AR game score, and sensors data value are published on the cloud via MQTT protocol. Moreover, fuzzy rules are checked, and corresponding output variables are updated and published on their topics. Then, according to which rule-bases are activated, the user receives different types of messages, or the user’s manual changes Time Early morning, morning, early afternoon, late Linguistic can be enabled. Distance Humidity afternoon, evening, night, midnight Near, far, very far Linguistic Very dry, dry, humid, very humid Linguistic The adaptive fuzzy decision-making algorithm is capable of scaling up to more rules to improve the user’s independence. The location of Temperature Very cold, cold, cool, mild, warm, hot, very hot Linguistic Reminder Yes, No Boolean Movement Low, medium, high Linguistic different objects and the most used ones can also be added to the database. Therefore, new rules or new objects can be added, or the other rules Flame detection Gas detection Yes, No Boolean Yes, No Boolean can be removed and updated by the caregivers remotely based on the knowledge and dependent of each user’s lifestyle, and they can manu - Relay status Yes, No Boolean Voice message 1, 2, … Integer ally start a scenario or send simple reminders to the user. The fuzzy rules must be endowed with a base of knowledge of each user provided by the Image 1, 2, … Integer caregiver, so all the activities are adapted to their p