A real-time smartphone- and smartshoe-based fall prevention system

Majumder, Akm Jahangir Alam

doi:10.1145/2554850.2555191

Cited by 7 publications

(26 citation statements)

References 4 publications

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“…These tests have to be conducted in a supervised environment and may therefore suffer from influences such as the Hawthorne effect, that is, the reaction in which the individual modifies his/her behavior due to his/her awareness of being observed. Unlike the clinical assessment tests, a real-time fall risk is predicted by recognizing an abnormal walking pattern, then the user is alerted [ 17 – 19 , 31 ], or an external aid such as a walker or robot is exploited to prevent the fall [ 20 , 21 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

Hemmatpour

Ferrero

Gandino

et al. 2018

Journal of Healthcare Engineering

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Falls are critical events for human health due to the associated risk of physical and psychological injuries. Several fall-related systems have been developed in order to reduce injuries. Among them, fall-risk prediction systems are one of the most promising approaches, as they strive to predict a fall before its occurrence. A category of fall-risk prediction systems evaluates balance and muscle strength through some clinical functional assessment tests, while other prediction systems investigate the recognition of abnormal gait patterns to predict a fall in real time. The main contribution of this paper is a nonlinear model of user gait in combination with a threshold-based classification in order to recognize abnormal gait patterns with low complexity and high accuracy. In addition, a dataset with realistic parameters is prepared to simulate abnormal walks and to evaluate fall prediction methods. The accelerometer and gyroscope sensors available in a smartphone have been exploited to create the dataset. The proposed approach has been implemented and compared with the state-of-the-art approaches showing that it is able to predict an abnormal walk with a higher accuracy (93.5%) and a higher efficiency (up to 3.5 faster) than other feasible approaches.

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Section: Background and Related Workmentioning

confidence: 99%

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

Hemmatpour

Ferrero

Gandino

et al. 2018

Journal of Healthcare Engineering

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“…Habib et al [ 35 ] noted that individuals may not place their smartphone in a garment of clothing that they are wearing whilst at home, limiting the ability of the smartphone to monitor physical movement in the home. That being said, older adults have shown a preference to place a smartphone in a pocket of their clothing rather than wear a dedicated device [ 138 ] when given the option, a preference which may extend to other subpopulations when asked to wear a non-invasive monitor for their health. At present, it is unclear if methods need to be developed to identify these periods when the smartphone is not placed on the body, as well as developing mechanisms or strategies to facilitate non-invasive monitoring during these times.…”

Section: Challengesmentioning

confidence: 99%

Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement

Rosario

Redmond

Lovell

2015

Sensors

176

126

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Advances in mobile technology have led to the emergence of the “smartphone”, a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that “count” steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to “close the loop” by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions.

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“…However, this work does not provide any information about falls severity regarding the surface where object hits. Similarly, the work presented in [Majumder et al 2013] describes the "iPrevention" system. Built inside a smartphone, it is in charge to detect elderly patients falls identifying high-risk ones.…”

Section: Related Workmentioning

confidence: 99%

“…Some related proposals try to recognize user behavior considering mobile devices inertial sensors, such as accelerometer and gyroscope. Data coming from these sensors may allow special applications infer whether a device/user is falling to the ground or not [Majumder et al 2013] [Mehner et al 2013]. Although imperative, knowing when a device/user is falling is not enough by itself without having surface dangerousness estimation.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Falls and Surfaces Using Mobile Devices

Amorim¹,

Mion²,

Pereira³

et al. 2017

Anais Do XLIV Seminário Integrado De Software E Hardware (SEMISH 2017)

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Mobile devices are getting much more relevance during the users' day, in a way that they are paying to increase device security and durability though external cases or insurance plans. However, these approaches are useless if the individuals does not properly take care of their devices. This paper describes an approach to monitor and classifies a surface where a smartphone falls, making possible to categorize this crash into a range of dangerousness. The authors collected empirical data from device falls to make possible the development of an optimal classifier. Our results reached up to 88% recognition rate of surfaces considering a specific features subset, letting us conclude that it is possible to infer user care level through the analysis of how a device is being treated.

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A real-time smartphone- and smartshoe-based fall prevention system

Cited by 7 publications

References 4 publications

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement

Recognizing Falls and Surfaces Using Mobile Devices

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