Information on fall-related events is usually collected through questionnaires, fall diaries, and phone calls. This information is often augmented with data collected from various sensors to improve reliability and accuracy. Fall prediction systems must capture the multifactorial nature of falls for reliable fall risk estimation. These include environmental, physiological, and psychological risk factors.
Fall detection systems mainly focus on physiological risk factors such as gait, mobility, and vision. Fall detection systems usually focus on developing a fall detection device using wearable sensors that can be integrated into watches, shoes, belts, etc.
Fall prediction systems focus on information fusion from both wearable and ambient sensors for reliable estimation of fall risk. These systems also include the design and evaluation of user interfaces such as smartphone applications for fall prevention intervention and educating subjects on fall risk factors.
Wearable health systems based on wireless body sensor networks BSNs are becoming increasingly popular for real time biomedical monitoring in static or mobile scenarios [ 9 ]. A comprehensive survey of BSNs is presented by Chen et al. The main objective of BSNs is to enable pervasive monitoring of physical activities and behaviors, as well as physiological and biochemical parameters of the patients during their daily activities. Vital signs are regularly collected and remotely monitored by medical professionals achieving a more autonomous caretaking system.
In such systems, on body sensors are wirelessly connected via a multi hop network to a dedicated sink node such as a tablet or a smart phone.
Sensors are worn by the subject in various forms such as shoes, eyeglasses, earring, clothing, gloves and a watch. Spatio-temporal variation in gait is a reliable indicator of fall risk, however, existing systems focus on gait analysis in clinical settings and are not geared towards continuous gait analysis in a home environment. Wearable BSNs can be used to address this challenge. These sensors, which may be accelerometers, gyrosensors, force sensors, or strain gauges can measure various characteristics of the human gait.
Signal processing algorithms and feature extraction techniques can be developed to process various gait features such as stride speed, stride length, and inter leg spacing for predicting a future fall. In this article, we describe the recent trends, challenges, and limitations in designing effective fall prediction and prevention systems. We present recommendations for overcoming these limitations and describe key focus areas for future research.
Fall detection and prediction systems can be broadly categorized into two types: context-aware systems and wearable devices. Context-aware systems use ambient sensors such as cameras, pressure sensors, vibration sensors, and infrared sensors to classify activities of daily living and predict falls [ 5 ]. Wearable systems use sensors such as accelerometers and gyroscopes for gait analysis and mobility monitoring [ 11 ]. Recent research on wearable technologies for fall detection and prediction has primarily focused on fusing data from accelerometers and gyroscopes for fall risk assessment [ 12 , 13 , 14 , 15 , 16 , 17 ].
Bourke et al. Their work concluded that a fusion of all three features gives the highest fall sensitivity and the lowest false positive rate when using a triaxial accelerometer. Bianchi et al.
They tested a variety of fall scenarios both indoors and outdoors with the objective of reducing false alarms. Their results showed that adding a barometric pressure sensor may prevent false positives under common scenarios of use.
However, similar to many studies reporting simulated falls, their work was only tested on healthy young adults. Comfort of wearable sensors is very important in fall detection and prediction systems since it entails long-term continuous use. A recent study reported that in a three-month home trial of case enclosed waist-mounted accelerometer among aging adults, subjects transferred the wearable device between various body locations due to bruising and discomfort [ 20 ].
Hence, an important requirement for wearable systems includes device size and comfort, not to cause bruising or discomfort over time, even if attached continuously in the same location.
Howcroft et al. Their work concluded that multi-sensor gait assessments provided the best input data for fall-risk prediction, using a combination of posterior pelvis, head, and left shank accelerometers and a neural network.
The best single-sensor model used a posterior pelvis accelerometer, dual-task gait data, and a neural network. Sannino et al. They perform windowing of the data to classify windows as being part of fall or non-fall actions, and a final window composition to assess whether or not each global action was a fall. Their approach was tested on real-world data consisting of fall and non-fall events. Their results are promising and provide a strong foundation for implementation of real-world fall detection systems.
Fortina and Gravina [ 23 ] developed a novel real-time non-invasive fall detection and alarm notification system using a wearable accelerometer and a smart phone. Their system is able to trigger fall events using different alerting modalities enabling prompt emergency interventions. This system provides a benchmark for the design and evaluation of future fall detection systems.
However, there is no research on developing unobtrusive wearable devices for constant measurement of blood pressure to detect orthostatic hypotension and the associated fall risk. Hence new research is needed to investigate the characteristics of wearable systems such as obtrusiveness, cost, and user friendliness to improve their appeal among older adults.
Camera-based sensors have been widely used in fall detection and prediction systems [ 24 , 25 ]. In such systems, multiple cameras are used to monitor the daily activities of persons in their home environment. Although camera-based systems provide detailed information on certain fall risk factors, they suffer from several drawbacks such as privacy, cost, and user acceptance. Proximity sensors are another example of ambient sensors used for detecting falls. Hirata et al.
The proximity sensors are attached to a walking aid device for measuring sudden changes in the movements and distance of a person from the proximity sensors. Such sensors have a short proximity range and a higher false alarm rate since a person stepping away from the walker can be misinterpreted as a fall.
Bian et al. The proposed fall detection approach uses an infra-red based depth camera that can operate in dark environments. However, their approach cannot detect falls ending lying on the furniture.
Hilbe et al. If the pressure sensor value exceeds a certain threshold, an alarm is sent to clinicians in order to prevent a fall from occurring.
Commercially available ambient sensors are often used to interact with falls prevention exercise games. For example, Pisan et al. Clinical fall risk assessments often involve questionnaires and functional assessments of posture, gait, cognition, and other fall risk factors [ 31 ].
These clinical assessments provide a snapshot overview of fall risks, but are often subjective, and use threshold assessment scores to classify people as fallers and non-fallers [ 32 , 33 ]. However, geriatric fall risk should be more accurately modeled using a continuum and multiple risk categories, such as low, moderate, and high fall risk.
Longitudinal monitoring of aging adults in a free-living environment using unobtrusive sensors and mobile health tools can provide a more accurate and objective assessment of fall risk. Shany et al. However, they did not discuss testing and validation of fall risk assessment methodologies and real-world implementations on frequent fallers. Tong et al. However, their method was not tested on real-world falls and aging adults who are frequent fallers. Table 1 shows a qualitative summary of various wearable and ambient sensor-based fall detection and prediction systems.
The three major research gaps in existing fall prediction systems can be summarized as follows:. Lack of a comprehensive fall prediction system. Existing systems do not address information fusion that captures contextual and physiological data from wearable and ambient sensors for fall risk estimation.
Dearth of user-friendly interfaces and feedback techniques to actively engage and empower patients towards effective techniques to prevent falls. No efficient web interfaces to help clinicians visualize health data and assess fall risk. Fall prediction involves the design of signal-processing techniques and machine-learning algorithms for reliable estimation of fall risk and providing timely alerts before the occurrence of a fall.
Fall prevention is focused on the design and implementation of techniques and intervention programs for mitigating fall risk factors, improving gait and mobility, and preventing a future fall. Examples of fall prevention intervention techniques include exercise, medication review, and home modification. Both fall prediction and prevention are complex multifactorial problems due to the interaction between physiological, behavioral, and environmental factors that contribute to a fall.
Fall risk assessment is an important technique that identifies intrinsic gait, muscle weakness, neurological or visual deficits, etc.
Figure 1 shows the various fall risk factors and the complex interactions between them. For instance, a combination of gait impairment, improper medication usage, muscle weakness, and slippery surfaces can substantially increase the risk of falling. This section describes current work and limitations of fall prevention systems. Existing fall prevention systems mainly focus on rehabilitation robotics and wearable devices to assist patients with gait impairments and prevent future falls.
Di et al. Their system uses the center of gravity of the user in conjunction with the cane sensors to determine fall risk. Majumber et al. The pressure sensors on the shoe along with accelerometer in the smart phone is used to collect gait and balance information. Their system uses a smart phone application that triggers an alarm when gait abnormalities are detected.
Future work should focus on information fusion from both wearable and ambient sensors for assessing a variety of fall risk factors such as vision and sleep disturbances, medications, and environmental hazards. A popular approach to falls prevention involves methods targeting the restoration of muscle strength and balance for prevention of fall risks [ 38 , 39 ].
Exercise interventions are becoming an increasingly popular approach for fall prevention and there is extensive literature supporting the effectiveness of these interventions in reducing falls and the risk of falling [ 40 ]. There are a wide range of falls prevention intervention systems focused on overcoming falls and reducing the risk of falling. Pre-falls prevention intervention systems Pre-FPIs focus on monitoring and supporting patients who have not yet experienced a fall, but may be considered to be at risk of falling.
These systems support the delivery of targeted physical activities and educational programs that increase awareness of fall risks and help develop strategies to identify and overcome environmental fall hazards. Pre-FPIs aim to overcome intrinsic fall risk factors such as vision, balance, muscle strength and cognitive decline [ 41 , 42 ].
With regard to intrinsic fall risk factors, functional ability was the main focus of a number of studies [ 43 , 44 , 45 ]. In these studies, various technologies were used to proactively mitigate observed deficits in functional ability. For example, Visvanathan et al. De Morais and Wickstrom [ 43 ] developed a game based technology using tai chi, to help improve the stability of subjects with balance impairments and impaired mobility.
Initially, subjects were given a demonstration of pre-recorded tai chi activities at the start of the game and are required to mimic those movements during gameplay. Post-fall prevention intervention systems Post-FPIs focus on developing technologies for persons who have already experienced a fall and deliver interventions to reduce the future occurrence of a fall [ 46 ].
Post-FPI supports the delivery of re-active interventions. These systems involve a diagnostic assessment function, whereby the cause of the fall, which triggered the post fall intervention, is identified along with other fall risk factors. Cross fall prevention intervention systems CFPIs are technologies that support and deliver a combination of pre-fall, post-fall and fall injury prevention interventions.
Shi et al. Their approach also uses traditional clinical tests and detects falls after they have occurred in order to prevent fall-related injuries. In their approach, older adults are assessed for intrinsic fall risks and are provided an exercise regime to reduce those intrinsic risks such as functional decline and a decline in muscle strength. Intervention techniques used for preventing fall risks are typically administered either by clinicians or self-administered by patients [ 49 , 50 , 51 , 52 ].
Physical activities are effective intervention techniques used to mitigate intrinsic fall risk factors.
Recent studies have demonstrated that virtual reality and gaming technologies are interactive and effective for patients to engage in exercise activity compared with more traditional approaches [ 49 , 50 , 51 , 52 ]. For example, Chao et al. Their results showed that the application of the self-efficacy theory to enhance exercise behavior to engage older adults in physical activities increases adherence rates of exercise programs. Post-FPI intervention techniques consist of functional assessment, cognitive assessment, and environmental assessment.
Functional assessment is the main intervention technique that is widely used to determine intrinsic fall risk factors such as functional ability deficits [ 54 , 55 , 56 , 57 , 58 ]. For instance, Majumder et al. The gait patterns were collected from users over a period of time while performing activities of daily living ADL such as walking. Staranowicz et al. Their approach identifies functional decline using an autonomous robot.
The systems proposed in [ 61 , 62 , 63 ] use both cognitive assessment and functional assessment techniques to assess functional ability deficits, balance and cognitive impairments. In these studies, patients perform physical activities and cognitively demanding tasks to determine fall risks. Du et al. This system is operated remotely by clinicians, to automate home assessments that are typically conducted by in-person visits.
The design of fall prediction and prevention systems faces several significant challenges. These are described below. Fall prediction and prevention systems need to be accurate, reliable, robust, and cost-effective. High specificity and sensitivity are the main goals of a reliable fall prediction system. This is sometimes achieved in experimental environments, but when applied to a real world setting, the performance is often unknown.
Fall detection devices are designed and tested under controlled conditions, for example they use data from falls and activities of daily living of young adults. These experiments are simulated at the discretion of authors due to the lack of a standardized procedure or a public database for comparison. Only few studies incorporate data from older people [ 65 , 66 ], although their participation is limited to perform a set of simulated activities of daily living for a few minutes or hours.
It is evident that existing systems have been mainly tested in laboratory environments with controlled conditions and do not include frequent fallers and aging adults as test subjects. Hence, future work should focus on longitudinal studies of fall detection and prediction systems in real life conditions on a diverse group that includes frequent fallers, aging adults, and persons with neurological disorders. Future work on IoT-enabled fall detection and prediction systems should address the long term assessment of comfort level and obtrusiveness of the wearable devices among older adults.
Fall prediction systems frequently use an IoT-enabled approach for pervasive monitoring. IoT enabled monitoring systems combine data from multiple sensors and transmit data wirelessly to a smart phone for pervasive monitoring and fall prediction. Such systems should incorporate user feedback and preference throughout the monitoring process. Existing monitoring systems mainly track and report data from wearable sensors and do not engage the users in the monitoring process.
Recent surveys have shown that wearable technology enabled systems for falls have little appeal among aging adults due to lack of feedback and user engagement [ 68 ].
User acceptance poses a major challenge since older adults may not be familiar with wearable devices and mobile health technologies. Recent work has focused on developing smartphone-based systems and mobile applications apps for fall detection and prediction [ 69 ]. However, these studies have not tested the usability and acceptability of the mobile apps among aging adults. Privacy, integrity, and confidentiality of data are major concerns in fall prediction systems that process and transmit sensitive information about patient health.
Context-aware systems and vision-based systems are much more prone to privacy concerns compared to wearable devices such as accelerometers and gyroscopes. Privacy issues pose a huge challenge in testing fall prediction systems in free living environments and community settings.
This is particularly challenging in IoT-enabled systems where patient sensitive health data are stored in smartphones and transmitted over wireless networks that are vulnerable to attacks. Recent work has focused on mobile cloud based systems in health care for data storage and processing to protect patient privacy [ 70 ]. However, such systems have not been adopted for fall prediction. A comprehensive fall prediction system should include an interdisciplinary approach that fosters a synergy between engineers, computer scientists, and healthcare professionals to ensure compliance with HIPPA regulations and standards on patient confidentiality.
The adoption of a cloud computing IoT paradigm provides several benefits for data security and privacy in the context of healthcare services and IoT-enabled fall prediction systems.
Identity privacy is an important concern in cloud based IoT systems. The technique of pseudonyms has been widely adopted to address identity privacy, but the periodically updated pseudonyms and certificates lead to intolerable computational cost for resource-constrained sensors. Location privacy is especially critical in IoT-enabled fall prediction systems, since the frequently exposed location privacy would disclose the living habit of the user.
The widely adopted technique in cloud based IoT systems is to hide the location through pseudonyms. However, since the location information is not directly protected, it cannot resist dynamic tracing attacks. Zhou et al. However, their work was only tested on a single user and further research is needed to test the efficiency of their techniques on multiple users. IoT-enabled fall prediction and prevention systems should incorporate energy-optimization techniques for conserving the battery power of sensors.
A limited energy budget is the primary constraint on smartphones and wearable sensors. To address this issue, future work should develop energy-optimization techniques such as power gating, sampling frequency scaling, and configurable operational modes to conserve the battery power of sensors [ 72 ].
Compressive sensing is an efficient technique for data acquisition and conserves energy by reducing the amount of wireless data transmission. Smartphone-based systems should address the storage and computational limitations of the smartphone to process Big data due to the limited battery power. In addition, mobile technologies such as smartphone apps that provide patient feedback may perform differently depending on the smartphone model in which they are installed.
This possibility should be investigated for successful implementation of mobile health technologies in real-world settings such as assisted living facilities. Cloud computing is particularly useful in IoT-enabled fall detection and prediction systems that often use battery constrained smartphones for implementing information fusion and machine learning algorithms. The adoption of cloud computing paradigm enables the execution of secure multimedia-based health services, thus eliminating the need for executing computationally intense multimedia and security algorithms on mobile devices with limited computational capacity and battery power.
Cloud computing provides a flexible storage and processing infrastructure for performing both online and offline analyses of large volumes of sensor data. However, achieving energy efficiency in both data transmission and processing is still an open research issue [ 73 ]. Techniques such as data caching mechanisms for reusing collected data in time-tolerant applications and middleware for compressing data in continuous and long-duration monitoring scenarios can be used to address this challenge.
The multifactorial nature of falls warrants a comprehensive interdisciplinary approach for effective fall prediction and prevention. Future research should focus on the following key areas. Contextual information about patient behavioral patterns and environment play a crucial role in predicting falls.
For instance, significant deviations in sleep patterns and medications, as well as gait abnormalities can indicate an underlying medical concern that may increase fall risk. Hence reliable estimation of fall risk necessitates information fusion from wearable and ambient sensors and a decision support system for meaningful inferences.
Gravina et al. The survey provides a systematic categorization and common comparison framework of the literature.
Here to demonstrate this credo are the bevy of midi skirts that dominated the look books and virtual presentations for the fall season. They are box-pleated at Christian Dior, knife-pleated at Erdem, A-lined at The Row, and flowy at Partow—but all reach midway between the knee and ankle.
Pictured from top to bottom: 3. The optimal word to describe fashion in the past year was cozy. Designers are continuing down this path for fall , but not in the way you would initially think—read: no sweats here. The fabrics may indeed be plush and the sizes large, but the silhouettes are much more tapered than what is normally expected of winterwear.
Indeed, cozy never looked so good. The trusted suit has gotten a revamp. Traditionally, the style is composed of a blazer-and-trouser combo; blazer and skirts have also become commonplace. For fall , designers introduced something new into the fold: coats. Instead of fitted jackets, Givenchy, Jason Wu, Proenza Schouler, and more showcased palazzo, paper-bag-waist, flared, or cropped trousers all with elongated toppers. Pictured from top to bottom: A Potts, 3.
The future is bright, bold, and primed for attracting attention. Here to herald this message are fashion designers. Come fall, we are going to break free from gray and neutral tones, and bring some vibrancy into our lives. They haven't left and never will. But what does change is the proportion. For fall , the trend de jour is short and super fitted. From tuxedo dresses at Fashion East to babydoll silhouettes at Christian Dior, the rule of thumb is to show legs for days, darling.
Bazaar Bride. United States. Type keyword s to search. Today's Top Stories. Goodbye to All That. Advertisement - Continue Reading Below. She's a Rich Girl. Sequined Velvet Midi Dress. Sergio Hudson. A Little Off Color. Lena Faux Leather Midi Skirt. Saks Fifth Avenue. Tailored on Tailored.
Pinstriped Wool Vest. Knit Wits. Striped Cashmere Maxi Dress. Shoppers who deem dressing up an essential task will find plenty of grittily glamorous frocks from Simone Rocha, Prada, Paco Rabanne, and Rick Owens, and cocooning couture shapes from Louis Vuitton, Patou, and Roksanda. For those who want to move through post-pandemic life with an unfussy ease, there are roomy new jeans at Christian Dior and Balenciaga and pleated skirt suits at Molly Goddard, Max Mara, and Calvin Luo.
Even monograms have toned it down, with new logo prints at Chanel, Versace, and Balmain. A rising generation of millennial power spenders has simultaneously dictated the return of escapist aughts nostalgia; forget the roaring twenties, when we return to parties, we will do it with the vigor—and itty-bitty, flitty little dresses—of Paris, Lindsay, and Nicole. As fashion adjusts to mirror our times, it must embrace smaller, more individually powerful notions of style.
That explains why, like everything else right now, so many of the collections are in total disagreement with one another: Show some skin or cover it up! Be comfortable or be crazy! One year ago, we were trying to predict what the s would look like. After this season, our bets are on a period of rebellious personal style and a rebirth of subcultures. That will give us something to talk about from behind our screens—or even better, together again in person soon. Designers like Gabriela Hearst, Marine Serre, and Stuart Vevers at Coach are patching together unused fabric scraps to produce coats, dresses, and upcycled tees.
These collaged garments are not only sustainably minded, but also nod to a new, idiosyncratic aesthetic that is less about head-to-toe dressing and more about personal expression through style. A brooding glamour is mounting across Europe, suggesting a reemergence look that is fabulous, but with a not insignificant bite.
Miuccia Prada and Raf Simons placed their bets on sequins and stoles, while Dries Van Noten is bringing back taffeta volumes and a little campy glitz. Tinsel at Rokh and surreal proportions at Marni round out the trend, promising a vampish scene come fall.
0コメント