Automated activity recognition and monitoring of elderly using wireless sensors: Research challenges

Ranasinghe, Damith C.; Torres, Roberto Luis Shinmoto; Wickramasinghe, Asanga

doi:10.1109/iwasi.2013.6576067

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…A prior study conducted by Ranasinghe et al [32] reveals the limitations of using wireless and vision sensors for monitoring the ADL for older people. According to the authors, there are three key issues in monitoring the ADL in older people.…”

Section: Frailty Assessment Using Adl and Mobilitymentioning

confidence: 99%

“…The vision sensor based approaches are widely reported in the literature to address risk of fall assessments, balance assessments and postural assessments [ 38 – 40 ]; yet such sensors are rarely used to assess frailty. However, vision sensors are preferred choice when patients or subjects have aversion to body-worn sensors while performing the tasks [ 32 ].…”

Section: Quantitative Assessmentmentioning

confidence: 99%

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Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

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Section: Frailty Assessment Using Adl and Mobilitymentioning

confidence: 99%

Section: Quantitative Assessmentmentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

View full text Add to dashboard Cite

show abstract

“…In [ 12 ] the possibility of improving ADL classification accuracy by applying feature ranking and selection is explored. Activity recognition was performed in [ 13 ] using a hidden Markov model on recordings from sensors placed in the house and on the body, whereas in [ 14 ] the aim was to identify high falls’ risk-related activities of older people based on a wearable passive radio frequency identification sensor. Analysis was based on data from healthy adult volunteers.…”

Section: Introductionmentioning

confidence: 99%

“…Although many frameworks have been reported in the literature for activity monitoring of older people, most of them have been tested on data from young and healthy participants [ 11 , 12 , 14 ], or the experiments were performed in laboratory conditions, e.g., in [ 13 ] a scaled model of a house used along with a simulated sequence of activities. Those works report high classification accuracy, but results are not directly comparable with uncontrolled monitoring systems in real home environments.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Papagiannaki

Zacharaki

Kalouris

et al. 2019

Sensors

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The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.

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“…For example, some researchers are developing a real-time environmental monitoring system for greenhouses that integrates mobile terminals and WIFI wireless communication to monitor parameters such as soil moisture, light intensity, air temperature, and humidity in the greenhouse [22]. They are using sensors and single-chip microcomputers to collect environmental indicators in the greenhouse [23][24][25][26][27][28][29]. Users can access real-time greenhouse environmental information using a mobile application monitoring interface.…”

Section: Introductionmentioning

confidence: 99%

Design of Tone Recognition System for Pipa Strings Based on Wireless Sensors

Pang¹

2022

Mobile Information Systems

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Pipa has been widely used as a wooden musical instrument as early as the Han period. It mainly relies on human fingers to pluck the strings to make sounds. The timbre of the pipa also has a very strong national characteristic. Understanding the meaning of words, phrases, and sentences requires tone recognition. For tone identification, the fundamental frequency conveys the most unique information. This paper summarizes the influence of the pipa’s characteristics, technology and skills of the player, and the player’s factors on the timbre of the pipa performance. In addition, given the problems of low accuracy and high data packet loss rate in the existing pipa playing tone recognition methods, this paper proposes a pipa string playing tone recognition algorithm based on wireless sensors. According to the sensor stage and weight-corresponding characteristics, on the premise of minimizing the total mean square error, the optimal weight corresponding to the sensor measurement value is found according to the adaptive mode. The unbiased estimated value of the measured value of each node is obtained through iterative calculation. The Euclidean distance between the measured value and the estimated value of each sensor obtained by the normalization process is used as the adaptive weighted recognition weight. It completes the conversion of the adaptive weighted recognition algorithm structure and the binary recognition result. The experimental results show that the proposed method has good tonal recognition accuracy in the pipa playing environment.

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Automated activity recognition and monitoring of elderly using wireless sensors: Research challenges

Cited by 17 publications

References 29 publications

Assessment of frailty: a survey of quantitative and clinical methods

Assessment of frailty: a survey of quantitative and clinical methods

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Design of Tone Recognition System for Pipa Strings Based on Wireless Sensors

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