SmartFall: An Automatic Fall Detection System Based on Subsequence Matching for the SmartCane

Lan, Mars; Nahapetian, Ani; Vahdatpour, Alireza; Au, Lawrence; Kaiser, William J.; Sarrafzadeh, Majid

doi:10.4108/icst.bodynets2009.5873

Cited by 49 publications

(38 citation statements)

References 37 publications

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“…Approaches reported in literature include inertial sensors embedded in a custom-designed vest (Bourke et al, 2008) and an inertial sensor embedded into a cane (Lan et al, 2009). Smartphones have also attracted attention as potential falls detection devices because of their integrated inertial sensors.…”

Section: Safety Monitoring and Falls Detectionmentioning

confidence: 99%

Wellness, Fitness, and Lifestyle Sensing Applications

McGrath¹,

Scanaill²

2013

Sensor Technologies

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Physical fitness is not only one of the most important keys to a healthy body; it is the basis of dynamicand creative intellectual activity. -John F. Kennedy, 35th President of the United StatesChapter 9 considered how sensors are playing an increasingly important role in health-related applications, such as chronic disease management. Medically, health is sometimes described as the absence of one or more of the "five Ds": death, disease, discomfort, disability, and dissatisfaction. Consequently, the focus is on determining whether disease is present and, when present, managing that condition (Edlin et al., 2000). Wellness takes a different perspective on health. It looks at the entire person, the manner in which they live their life, and lifestyle influences on their well-being. Wellness encompasses six distinct dimensions of well-being: emotional, intellectual, spiritual, occupational, social, and physical (Hettler, 1976). Collectively, these dimensions are often referred to as the holistic model of wellness. Sensors can be applied to quantify all dimensions of wellness to a certain extent. Of these dimensions, only physical wellness is monitored by individuals in the consumer domain. This chapter focuses on physical well-being and how sensing can be used to monitor and maintain physical wellness. Positively influencing physical well-being can also have significant benefits for other aspects of well-being, such as socializing with others during physical activities and helping to reduce emotional stress.A variety of factors can influence personal wellness, including diet, exercise, poor habits, proactive self-care, and seeking medical intervention when appropriate (Edlin et al., 2014). As such, wellness is a dynamic process that is constantly changing based on the daily decisions we make about what we eat, drink, how much exercise we do, and so on. It is easy to lose track of wellness with the demands of busy, modern lifestyles. Technology is now having a positive effect on individuals by helping them to manage their physical wellness. This trend will continue to grow in the future as sensing and supporting technologies are seamlessly integrated into our daily lives. Discrete sensors and sensors integrated into smartphones are already enabling us to monitor our activity levels, fitness, performance levels, and calorie burn/consumption through smartphone apps and web portals. In a broader context, pervasive sensing in our homes and leisure areas will provide passive monitoring on a long-term basis of our physical activities, interactions with our environment, and other physiological, cognitive, and biochemical parameters of interest without activity restriction and behavior modification. The collected data can be used to notify us of immediate risk or to identify trends in parameters that are outside of normative ranges. Sensing sleep quality, in babies and adults, is a now a common application of pervasive sensing. The consumer does not actively track their sleep in real time but they need to be alerted immedi...

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Section: Safety Monitoring and Falls Detectionmentioning

confidence: 99%

Wellness, Fitness, and Lifestyle Sensing Applications

McGrath¹,

Scanaill²

2013

Sensor Technologies

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“…Oates, et al study clustering of signals for robotics [16]. As another form of activity detection, the Smartfall system attempts to detect falls through the use of accelerometers and gyroscopic sensors embedded in a cane [11] [2].…”

Section: Related Workmentioning

confidence: 99%

Shrinking symbolic regression over medical and physiological signals

Macbeth

Sarrafzadeh

2010

2010 2nd International Conference on Signal Processing Systems

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Medical embedded systems of the present and future are recording vast sets of data related to medical conditions and physiology. Linear modeling techniques are proposed as a means to help explain relationships between two or more medical or physiological signal measurements from the same human subject. In this paper a statistical regression algorithm is explored for use in medical monitoring, telehealth, and medical research applications.An essential element in applying linear modeling to physiological data is determining functional forms for the predictor signals. In this paper we demonstrate an efficient method for symbolic regression and model selection among possible transformation functions for the predictor variables. The three-stage method uses LASSO shrinkage regression to select a brief functional form and performs an polynomial lag regression with this form.This method is applied to medical and physiological time series data exploring the link between respiration and blood oxygen saturation percentage in sleep apnea patients. We found that our method for selecting a functional transformation of the predictor variable dramatically improved the goodness of fit of the model according to standard analysis of variance measures. In the dataset examined, the model achieved a multiple R 2 of 0.3373, while a plain time-lagged model without transformation or polynomial lags had a R 2 of only 0.016. All of the variables in the model produced by the algorithm had high scores in t tests for validity.

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“…Some of the work focuses specifically on detecting fall events [16][17][18], and others investigated fall-detection using walking aids [19][20][21]. Kwapisz et al [10] obtained high accuracy in classifying five different human activities -walking, jogging, stair-ascent and stair-descent, sitting and standing-based on the three-component acceleration of a cell phone within the subject's pocket.…”

Section: Introductionmentioning

confidence: 99%

“…Choi et al [16] distinguished falls from non-falls using inertial data collected from a belt worn around the waists of subjects. Lan et al [19] developed the SmartFall system for the SmartCane [22,23] Culmer et al [24] developed the iWA (instrumented walking aid) for the purpose of objective, quantitative clinical evaluation. They designed a custom load cell for the base of the cane to precisely measure axial load applied by the user.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an instrumented cane for gait recognition

Wade

Beccani

Myszka

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Independent mobility is an important aspect of an individual's life and must sometimes be augmented by use of an assistive device such as a wheeled walker or cane following a fall, injury, or functional decline. Physical therapists perform functional gait assessments to gauge the probability of an individual experiencing a fall and often recommend use of a walker, cane, or walking stick to decrease fall risk. Our team has developed a clinical assessment tool centered on a standard walking cane embedded system that can enhance a therapist's observation-based gait assessment with use of additional objective and quantitative data. This system can be utilized to detect timing and speed of cane placement, angular acceleration of the cane, and amounts of weight borne on the cane . This system is designed to assist physical therapists at the basic level in collection of objective data during gait analysis, to facilitate appropriate assistive gait device prescription, to provide patients and therapists feedback during gait training, and to reduce wrist and shoulder injuries with cane usage. However, more importantly, using the plethora of objective data that can be obtained from this cane, automated gait analysis and gait pattern classification can be performed to understand a patient's walking performance.

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SmartFall: An Automatic Fall Detection System Based on Subsequence Matching for the SmartCane

Cited by 49 publications

References 37 publications

Wellness, Fitness, and Lifestyle Sensing Applications

Wellness, Fitness, and Lifestyle Sensing Applications

Shrinking symbolic regression over medical and physiological signals

Design and implementation of an instrumented cane for gait recognition

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