A. Bourke

Fall detection with body-worn sensors A systematic review

Background and aimsFalls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics).MethodsThe searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96xa0records (33xa0journal articles, 60xa0conference proceedings and 3xa0project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA).ResultsThe main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies.ConclusionLimited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition.ZusammenfassungEinleitungStürze älterer Menschen stellen eine große Aufgabe für das Gesundheitswesen dar. Am Körper getragene Sensoren helfen, die Kinematik und Mechanismen von Stürzen besser zu verstehen. Ziel dieses Reviews ist es, Informationen aus publizierten Studien und deren Charakteristika (Sturzdokumentation und technische Spezifikationen) zu sammeln, zu extrahieren und kritisch zu diskutieren.MethodenDie systematische Suche innerhalb der öffentlich zugänglichen, elektronischen Literaturdatenbanken nach Artikeln zur Sturzerkennung mit am Körper getragenen Sensoren ergab 96xa0Publikationen (33xa0Fachzeitschriftenartikel, 60xa0Konferenzbeiträge und 3xa0Projektberichte), die von 1998 bis 2012 veröffentlicht wurden. Diese Publikationen wurden von jeweils zwei unabhängigen Gutachtern analysiert. Dabei wurden die relevanten Daten elektronisch erfasst und mit SPSS ausgewertet.ErgebnisseDie wichtigsten Erkenntnisse sind eine mangelnde Übereinstimmung in Methodik und Dokumentation (Studien- und technische Charakteristika sowie Sturzdokumentation) und ein substanzieller Mangel an Aufzeichnungen von realen Stürzen. In den meisten Publikationen fehlte eine etablierte Sturzdefinition. Die verwendeten Sensortypen sowie deren technische Spezifikationen variierten erheblich innerhalb der untersuchten Studien.SchlussfolgerungenEs wurde eine begrenzte methodische Übereinstimmung bei der sensorbasierten Sturzerkennung festgestellt. Es ist keine publizierte Evidenzbasis für kommerziell erhältliche Sturzerkennungsgeräte vorhanden. Ein Konsens von Forschergruppen weltweit wird notwendig sein, um fundamentale Fragen, z.xa0B. zur Sturzverifikation, zu erörtern, Leitlinien für eine Sturzdokumentation zu erarbeiten und eine gemeinsame Sturzdefinition zu entwickeln.

Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors

Falls are by far the leading cause of fractures and accidents in the home environment. The current Cochrane reviews and other systematic reviews report on more than 200 intervention studies about fall prevention. A recent meta-analysis has summarized the most important risk factors of accidental falls. However, falls and fall-related injuries remain a major challenge. One novel approach to recognize, analyze, and work better toward preventing falls could be the differentiation of the fall event into separate phases. This might aid in reconsidering ways to design preventive efforts and diagnostic approaches. From a conceptual point of view, falls can be separated into a pre-fall phase, a falling phase, an impact phase, a resting phase, and a recovery phase. Patient and external observers are often unable to give detailed comments concerning these phases. With new technological developments, it is now at least partly possible to examine the phases of falls separately and to generate new hypotheses.The article describes the practicality and the limitations of this approach using body-fixed sensor technology. The features of the different phases are outlined with selected real-world fall signals.ZusammenfassungStürze sind die mit Abstand häufigsten Ursachen von Frakturen und häuslichen Verletzungen im Alter. In den Cochrane Reviews und anderen systematischen Analysen wurden mehr als 200 randomisierte Interventionsstudien zur Sturzprävention erfasst. Eine neue Metaanalyse liegt für die Risikofaktoren von Stürzen vor. Dennoch bleiben Stürze und sturzbedingte Verletzungen eine große Herausforderung. Ein neuer Ansatz zur Erkennung, Analyse und Prävention von Stürzen ist es, Stürze in Abschnitte aufzuteilen. Dies könnte bei der Erstellung diagnostischer und präventiver Ansätze helfen. Phänomenologisch ist offenkundig, dass es eine Vorphase, Fallphase, Aufprallphase, Ruhephase und mögliche Erholungsphase gibt. Patienten und Fremdbeobachter sind allerdings nicht in der Lage, hierzu exakte Angaben zu machen. Durch technologische Neuentwicklungen ist es nunmehr möglich, diese Abschnitte zumindest teilweise zu beurteilen und daraus erste Hypothesen abzuleiten.Der Artikel beschreibt dabei die Praktikabilität und Beschränkungen der Verwendung von am Körper getragenen Sensoren. Die Sturzphasen werden anhand von Fallbeispielen verdeutlicht.

The complexity of daily life walking in older adult community-dwelling fallers and non-fallers

Complexity of human physiology and physical behavior has been suggested to decrease with aging and disease and make older adults more susceptible to falls. The present study investigates complexity in daily life walking in community-dwelling older adult fallers and non-fallers measured by a 3D inertial accelerometer sensor fixed to the lower back. Complexity was expressed using new metrics of entropy: refined composite multiscale entropy (RCME) and refined multiscale permutation entropy (RMPE). The study re-analyses data of 3 days daily-life activity originally described by Weiss et al. (2013). The data set contains inertial sensor data from 39 older persons reporting less than 2 falls and 32 older persons reporting two or more falls during the previous year. The RCME and the RMPE were derived for trunk acceleration and velocity signals from walking epochs of 50s using mean and variance coarse graining of the signals. Discriminant abilities of the entropy metrics were assessed using a partial least square discriminant analysis. Both RCME and RMPE successfully distinguished between the daily-life walking of the fallers and non-fallers (AUC>0.8) and performed better than the 35 conventional gait features investigated by Weiss et al. (2013). Higher complexity was found in the vertical and mediolateral directions in the non-fallers for both entropy metrics. These findings suggest that RCME and RMPE can be used to improve the assessment of fall risk in older people.

Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring

Despite its medical relevance, accurate recognition of sedentary (sitting and lying) and dynamic activities (e.g. standing and walking) remains challenging using a single wearable device. Currently, trunk-worn wearable systems can differentiate sitting from standing with moderate success, as activity classifiers often rely on inertial signals at the transition period (e.g. from sitting to standing) which contains limited information. Discriminating sitting from standing thus requires additional sources of information such as elevation change. The aim of this study is to demonstrate the suitability of barometric pressure, providing an absolute estimate of elevation, for evaluating sitting and standing periods during daily activities. Three sensors were evaluated in both calm laboratory conditions and a pilot study involving seven healthy subjects performing 322 sitting and standing transitions, both indoor and outdoor, in real-world conditions. The MS5611-BA01 barometric pressure sensor (Measurement Specialties, USA) demonstrated superior performance to counterparts. It discriminates actual sitting and standing transitions from stationary postures with 99.5% accuracy and is also capable to completely dissociate Sit-to-Stand from Stand-to-Sit transitions.

Fall detection with body-worn sensors

Background and aimsFalls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics).MethodsThe searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96xa0records (33xa0journal articles, 60xa0conference proceedings and 3xa0project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA).ResultsThe main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies.ConclusionLimited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition.ZusammenfassungEinleitungStürze älterer Menschen stellen eine große Aufgabe für das Gesundheitswesen dar. Am Körper getragene Sensoren helfen, die Kinematik und Mechanismen von Stürzen besser zu verstehen. Ziel dieses Reviews ist es, Informationen aus publizierten Studien und deren Charakteristika (Sturzdokumentation und technische Spezifikationen) zu sammeln, zu extrahieren und kritisch zu diskutieren.MethodenDie systematische Suche innerhalb der öffentlich zugänglichen, elektronischen Literaturdatenbanken nach Artikeln zur Sturzerkennung mit am Körper getragenen Sensoren ergab 96xa0Publikationen (33xa0Fachzeitschriftenartikel, 60xa0Konferenzbeiträge und 3xa0Projektberichte), die von 1998 bis 2012 veröffentlicht wurden. Diese Publikationen wurden von jeweils zwei unabhängigen Gutachtern analysiert. Dabei wurden die relevanten Daten elektronisch erfasst und mit SPSS ausgewertet.ErgebnisseDie wichtigsten Erkenntnisse sind eine mangelnde Übereinstimmung in Methodik und Dokumentation (Studien- und technische Charakteristika sowie Sturzdokumentation) und ein substanzieller Mangel an Aufzeichnungen von realen Stürzen. In den meisten Publikationen fehlte eine etablierte Sturzdefinition. Die verwendeten Sensortypen sowie deren technische Spezifikationen variierten erheblich innerhalb der untersuchten Studien.SchlussfolgerungenEs wurde eine begrenzte methodische Übereinstimmung bei der sensorbasierten Sturzerkennung festgestellt. Es ist keine publizierte Evidenzbasis für kommerziell erhältliche Sturzerkennungsgeräte vorhanden. Ein Konsens von Forschergruppen weltweit wird notwendig sein, um fundamentale Fragen, z.xa0B. zur Sturzverifikation, zu erörtern, Leitlinien für eine Sturzdokumentation zu erarbeiten und eine gemeinsame Sturzdefinition zu entwickeln.

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: A machine learning approach

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls

BackgroundReal-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls.ResultsBetween January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58xa0% additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request.ConclusionsThe FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease.

Towards holistic free-living assessment in Parkinson's disease: Unification of gait and fall algorithms with a single accelerometer

Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm. We tested the unified algorithms on a cohort of young healthy adults within a laboratory. We then deployed the algorithms on longitudinal (7 day) accelerometer-based data from an older adult with Parkinsons disease (PD) to quantify real world gait and falls. We compared instrumented falls to a self-reported falls diary to test algorithm efficiency and discuss the use of unified algorithms to impact free-living assessment in PD where accurate recognition of gait may reduce the number of automated detected falls (38/week). This informs ongoing work to use gait and related outcomes as pragmatic clinical markers.Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm. We tested the unified algorithms on a cohort of young healthy adults within a laboratory. We then deployed the algorithms on longitudinal (7 day) accelerometer-based data from an older adult with Parkinsons disease (PD) to quantify real world gait and falls. We compared instrumented falls to a self-reported falls diary to test algorithm efficiency and discuss the use of unified algorithms to impact free-living assessment in PD where accurate recognition of gait may reduce the number of automated detected falls (38/week). This informs ongoing work to use gait and related outcomes as pragmatic clinical markers.

Designing Smart Home Technology for Fall Prevention in Older People

Falls in older people constitute one of the major challenges in healthcare. It is important to design technologies that can help prevent falls and improve falls management. Smart home technology could be of importance in this context, but the technology has to be user-centred or adapted to be useful in this particular context. This study assessed usability of paper and interactive prototypes of a smart home touch screen panel. The study implemented five scenarios related to fall risk, fall assessment and exercise guidance, designing a smart home interface for independent living in general and fall management in particular. A usability evaluation showed that older people had positive experiences when using the touch screen interface. The study demonstrated the need for user-centred interfaces for older people in the context of falls prevention.

A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set.

Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.