Contactless Fall Detection for the Elderly

Nahian, Md. Jaber Al; Raju, Mehedi Hasan; Tasnim, Zarin; Mahmud, Mufti; Ahad, Md. Atiqur Rahman; Kaiser, M. Shamim

doi:10.1007/978-3-030-68590-4_8

Cited by 14 publications

(8 citation statements)

References 113 publications

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“…Using sensors to detect, react to, and mitigate falls is relevant to improving the quality of life of older adults [ 11 , 12 , 13 , 14 , 15 ]. In this study, we take a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults.…”

Section: Related Workmentioning

confidence: 99%

“…Nahian et al [ 12 ] discussed different methods and systems designed for detecting falls in older adults based on non-contact and wireless sensors. The authors presented a technical and in-depth look at the different technologies and described the definition of a model that responds to fall monitoring and control needs for adults and/or people with pathology that require specific care.…”

Section: Related Workmentioning

confidence: 99%

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Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

Márquez

Veloz

Minonzio

et al. 2022

Sensors

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The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

Márquez

Veloz

Minonzio

et al. 2022

Sensors

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“…Embedded sensors include those that are incorporated into a device such as a mobile phone, watch, or into clothing (10). While these wearable sensors have the advantages of monitoring and collecting data regardless of the users' location, they have some disadvantages related to privacy, security, robustness, accuracy, and technology-dependency of the users (11). Ambient sensors are attached to objects in the environment with which the user interacts (e.g., door, kettle, ceiling, walls).…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes

García-Constantino

Orr

Synnott

et al. 2021

Front. Digit. Health

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There is a global challenge related to the increasing number of People with Dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring and interventions have been investigated comprehensively in the literature. The objective of this study was to investigate the challenges with the design and deployment of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future SHIB implementations to have an adequate and prompt deployment allowing research to focus on the data collection and analysis aspects. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. The SHIB was evaluated via installation in the rooms of PwD with varying degrees of dementia at Kirk House Care Home in Belfast. Sensors from the SHIB were installed to test their capabilities for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data from the sensors were collected, stored, and handled using a ‘SensorCentral’ data platform. The results of this study highlight challenges and opportunities that should be considered when designing and implementing a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support monitoring the wellbeing of PwD. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of PwD were also not designed for an appropriate integration with ambient sensors. This study provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach.

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“…In recent years, advanced microelectromechanical systems (MEMS) and information and communication technology (ICT) create new opportunities for fall-related healthcare applications [ 4 , 5 ], including fall detection and prevention. Various sensors (e.g., inertial sensors [ 6 ], pressure or seismic sensors [ 7 , 8 ] and cameras [ 9 , 10 ]) and machine learning techniques (e.g., support vector machine (SVM), and k-nearest neighbor (kNN)) have been successfully applied to fall-related applications [ 5 , 11 , 12 , 13 , 14 ]. These works have shown that fall events can be automatically detected by the systems.…”

Section: Introductionmentioning

confidence: 99%

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Hsieh

Huang²,

Liu

et al. 2021

Sensors

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Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.

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Contactless Fall Detection for the Elderly

Cited by 14 publications

References 113 publications

Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

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