A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept
- It presents a simplistic design paradigm for an AAL system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. A comprehensive comparative study with prior works in the fields of indoor localization and fall detection is presented in this paper, which shows how this proposed system outperforms prior works in the fields of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design.
- The development of this system is highly cost-effective. We present a second comparative study where we compare the cost of our system with the cost of prior works in these fields, which involved real-world development. This comparative study upholds the fact that the cost of our system is the least as compared to all these works, thereby upholding its cost-effective nature. For this comparative study, we used only the cost of equipment as the grounds for comparison. While there can be several other costs (such as the cost of installation, cost of maintenance, salary of research personnel, cost of deployment, computational costs, and so on) that can be computed, most of the prior works in this field reported only the cost of equipment, so only this parameter was used as the grounds for comparison in this comparative study. Furthermore, comparing the cost of the associated equipment to comment on the cost-effectiveness of the underlying system is an approach that has been followed by several researchers in the broad domain of IoT.
2. Literature Review
- The available AAL-based systems for fall detection cannot track the user’s indoor location, and vice versa. Furthermore, the hardware components (sensors) used to develop the fall detection systems cannot be programmed or customized to capture the necessary data required for incorporating the functionality of indoor localization in such systems, and vice versa. For instance, a host of beacons , WiFi access points , and WiFi fingerprint capturing architecture  help to capture the necessary data for indoor localization, but these hardware components cannot be programmed or customized to capture any relevant data that would be necessary for detecting falls. It is highly essential that, in addition to being able to track, analyze, and interpret human behavior, such systems are also able to detect the associated indoor location so that the same can be communicated to caregivers or emergency responders to facilitate timely care in the event of a fall or any similar health-related emergencies. Delay in care from a health-related emergency, such as a fall, can have both short-term and long-term health-related impacts.
- A majority of these systems were tested on datasets [24,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80]. The proposed software designs and/or software frameworks that were used by the authors of these respective works cannot be directly applied in the real-world to detect falls and indoor locations of users during ADLs as the underlining systems were not developed to work based on incoming or continuously generating human-behavior-based data in real-time.
- While there have been some works that have involved the real-world implementation of the underlining AAL systems, the cost of equipment necessary for the development of such systems is very high. For instance, the system proposed by Kohoutek et al.  costs USD 9000, the one by Muffert et al.  costs more than USD 10,000, the one by Tilsch et al.  costs about USD 1055, the one by Habbecke et al. costs about USD 1055, the one by Popescu et al.  costs USD 1500, the one by Huang et al.  costs USD 750, the one by Dasios et al.  costs USD 581, and so on. Such high costs are a major challenge to the real-world development and wide-scale deployment of such systems across multiple smart homes.
- These methodologies use multiple sensors and hardware systems that need to be installed in the living confines of the user. Some examples include 13 beacons , WiFi access points and WiFi fingerprint capturing architecture [26,27], RSSI data capturing methodologies [28,29], thermal vision sensors , and smart cameras  that need to be carried by the users. Installing such sensors across smart communities or smart cities that could represent multiple interconnected smart homes would be highly costly, and the elderly are usually receptive to the introduction of such a host of hardware components into their living environments .
- The design process for the development of most of these systems [23,26,27,28,29,33,34,35,36,37,38,39,40,41,42,47,48,49,50,51,52,53,54,55,56,57] is complicated as it involves the integration and communication of multiple software and hardware components. As there is a need for the development of AAL systems that can perform both fall detection and indoor localizations in a simultaneous manner in the real-world, integrating the hardware components of these underlining systems (integrating hardware components from systems aimed at fall detection with hardware components from systems aimed at indoor localization) and developing a software framework that can receive, communicate, share, and exchange data with all these hardware components in a seamless manner in real-time would be even more complicated.
- Some of the works have also involved the development of new applications, such as the smartphone-based application proposed in  and the wearable devices proposed in [46,49,50]. Replicating the design of an application has several challenges unless it is replicated or re-developed by the original developers . In the context of wearables, it is crucial to ensure that the design methodology follows the ‘wearables for all’ design approach . Both these factors pose a challenge to the mass development of such solutions.
3. Methodology and System Design
3.1. Methodology for Fall Detection during ADLs
3.2. Methodology for Indoor Localization during ADLs
- The complex activity analysis as per  involves detecting and analyzing the ADLs in terms of the atomic activities, context attributes, core atomic activities, core context attributes, start atomic activities, start context attributes, end atomic activities, and end context attributes using probabilistic reasoning principles and the associated weights of each of these components of a given ADL.
- Inferring the semantic relationships between the changing dynamics of these actions and the context-based parameters of these actions.
- Studying and analyzing the semantic relationships between the accelerometer data, gyroscope data, and the associated actions and the context-based parameters of these actions (obtained from the complex activity analysis) within each ‘activity-based zone’.
- Interpreting the semantic relationships between the accelerometer data, gyroscope data, and the associated actions and the context-based parameters of these actions across different ‘activity-based zones’ based on the sequence in which the different ADLs took place and the related temporal information.
- Integrating the findings from Step 4 and Step 5 to interpret the interrelated and semantic relationships between the accelerometer data and the gyroscope data with the location information associated with different ADLs that were successfully completed in all the ‘activity-based zones’ in the given indoor environment.
- Splitting the data into the training set and test set and developing a machine-learning-based model to detect the location of a user in terms of these spatial ‘zones’ based on the associated accelerometer data and gyroscope data.
- Computing the accuracy of the system using a confusion matrix.
3.3. Methodology for Indoor Localization and Fall Detection: Real-World Implementation
- The Imou Bullet 2S Smart Camera: The Imou Bullet 2S is a smart camera that can directly connect to WiFi and can be used to capture different components of video-based and image-based data during different ADLs. It has features such as infrared mode, color mode, smart mode, and human detection. The technical specifications of this smart camera include 1080P Full HD glass optics, 2.8 mm lens, 120° viewing angle, 98 ft night vision, IR lighting, inbuilt image-processing algorithm, storage facility via the H.265 compression system on an SD card (up to 256 GB) or on an encrypted cloud server, human motion detection, and an in-built microphone .
- The Sleeve Sensor Research Kit: The Sleeve Sensor Research Kit has several components to record the different characteristics of motion and behavior data during ADLs. These include an accelerometer, gyroscope, magnetometer, sensor fusion, pressure sensor, and temperature sensor. Specifically, this MMS sensing system consists of a wearable device with the following sensors: six-axis accelerometer + gyroscope, BMI270 temperature, BMP280 LTR-329ALS, BMP280 barometer/pressure/altimeter, ambient light/luminosity magnetometer, with three axes, BMM150 sensor fusion, nine-axis BOSCH 512MB memory, lithium-ion rechargeable battery, Bluetooth low energy, CPU, button, LED, and GPIOs .
- The Estimote Proximity Beacons: The Estimote Proximity Beacons can be used to track the proximity of a user to different context parameters as well as to detect the presence or absence of the user in a specific ‘activity-based zone’ during different ADLs. Each beacon has a low-power ARM® CPU (32-bit or 64 MHz CPU); a quad-core, 64-bit, 1.2 GHz CPU in Mirror flash memory to store apps and data; 8 GB in Mirror RAM memory for the apps to use while running; 1 GB in Mirror; and a Bluetooth antenna and chip to communicate with other devices and between the beacons themselves .
- They are cost-effective.
- These sensors are easily available and can be seamlessly set up in any given indoor space or region without the need for researchers to complete any intensive trainings.
- The development of a software solution that can communicate and interface with all these sensors is not complicated.
- The design process, both for the experiments and for the system architecture, becomes convenient owing to the specifications, coverage area, and characteristics of these sensors.
4. Results and Discussions
4.1. Results and Findings from Real-World Implementation
4.2. Comparative Study to Uphold the Cost-Effectiveness of the System
5. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Sensor||Cost Per Unit (in USD)||Total Cost (in USD)|
|Imou Smart Camera||29.99||59.98|
|Mbient Labs Sleeve Sensor||103.99||103.99|
|Microsoft SQL Server 11.0||0.00||0.00|
|The total cost of all the sensors||262.97|
|Work||Number of Human Subjects|
|Godfrey et al. ||1|
|Liu et al. ||1|
|Dinh et al. ||1|
|Dinh et al. ||1|
|Townsend et al. ||1|
|Cordes et al. ||1|
|Dombroski et al. ||1|
|Ichikari et al.  (VDR Track)||1|
|Lemic et al. ||1|
|Thakur et al. [this work]||1|
|Focus Area of Work||Author Names||Fall Detection||Indoor Localization|
|Effective Software Design||Effective Hardware Design||Effective Software Design||Effective Hardware Design|
|Indoor Localization||Varma et al. ||✓||✓|
|Indoor Localization||Qin et al. ||✓|
|Indoor Localization||Musa et al. ||✓||✓|
|Indoor Localization||Yim et al. ||✓||✓|
|Indoor Localization||Hu et al. ||✓||✓|
|Indoor Localization||Poulose et al. ||✓||✓|
|Indoor Localization||Barsocchi et al. ||✓||✓|
|Indoor Localization||Kothari et al. ||✓||✓|
|Indoor Localization||Wu et al. ||✓||✓|
|Indoor Localization||Gu et al. ||✓||✓|
|Fall Detection||Rafferty et al. ||✓||✓|
|Fall Detection||Ozcan et al. ||✓||✓|
|Fall Detection||Khan et al. ||✓||✓|
|Fall Detection||Cahoolessur et al. ||✓||✓|
|Fall Detection||Godfrey et al. ||✓||✓|
|Fall Detection||Liu et al. ||✓||✓|
|Fall Detection||Dinh et al. [49,50]||✓||✓|
|Fall Detection||Townsend et al. ||✓||✓|
|Fall Detection||Hsu et al. ||✓||✓|
|Fall Detection||Yun et al. ||✓||✓|
|Fall Detection||Nguyen et al. ||✓||✓|
|Fall Detection||Huang et al. ||✓||✓|
|Indoor Localization||Song et al. ||✓|
|Indoor Localization||Kim et al. ||✓|
|Indoor Localization||Jang et al. ||✓|
|Indoor Localization||Wang et al. ||✓|
|Indoor Localization||Wei et al. ||✓|
|Indoor Localization||Wietrzykowski et al. ||✓|
|Indoor Localization||Panja et al. ||✓|
|Indoor Localization||Yin et al. ||✓|
|Indoor Localization||Patil et al. ||✓|
|Indoor Localization||Gan et al. ||✓|
|Indoor Localization||Hoang et al. ||✓|
|Indoor Localization||Seçkin et al. ||✓|
|Fall Detection||Galvão et al. ||✓|
|Fall Detection||Sase et al. ,||✓|
|Fall Detection||Li et al. ,||✓|
|Fall Detection||Theodoridis et al. ,||✓|
|Fall Detection||Abobakr et al. ,||✓|
|Fall Detection||Abdo et al. ,||✓|
|Fall Detection||Sowmyayani et al. ,||✓|
|Fall Detection||Kalita et al. ,||✓|
|Fall Detection||Soni et al. ,||✓|
|Fall Detection||Serpa et al. ,||✓|
|Fall Detection||Lin et al. ||✓|
|Fall Detection||Thakur et al. ||✓|
|Indoor Localization||Thakur et al. ||✓|
|Indoor Localization and Fall Detection||Thakur et al. [this work]||✓||✓||✓||✓|
|Work||Costs (in USD)|
|Muffert al. ||>10,000|
|Kohoutek et al. ||9000|
|Popescu et al. ||1500.00|
|Yun et al. ||1372.49 *|
|Tilch et al. ||1055.98 *|
|Habbecke et al. ||1055.98 *|
|Hsu et al. ||950.21 *|
|Liu et al. ||844.61 *|
|Huang et al. ||750.00|
|Dasios et al. ||581.00|
|Braun et al. ||460.00|
|Nguyen et al. ||422.36 *|
|Thakur et al. [this work]||262.97|
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Thakur, N.; Han, C.Y. A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept. Information 2022, 13, 363. https://doi.org/10.3390/info13080363
Thakur N, Han CY. A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept. Information. 2022; 13(8):363. https://doi.org/10.3390/info13080363Chicago/Turabian Style
Thakur, Nirmalya, and Chia Y. Han. 2022. "A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept" Information 13, no. 8: 363. https://doi.org/10.3390/info13080363