Patent Description:
Unintentional falls account for greater than <NUM>,<NUM> annual deaths within the US population. Seniors are most vulnerable to falling and, as a result, suffer more than <NUM>,<NUM> hip fractures a year. Of those who fracture a hip, <NUM>% will never return to their homes. The poor balance that contributes to these fall events often declines for decades in advance of the fall event, yet the conventional method for tackling poor balance is to seek medical diagnostics and interventions only after a fall has occurred or the patient has a very serious balance problem. In fact, the current best predictor of a fall is whether someone has already fallen.

To truly improve the statistics of falls across the country, preventive intervention should be performed in advance of the first fall. Balance is similar to other physical performances, it can be improved with practice and, conversely, deteriorates with disuse. A number of lifestyle and health factors are known to influence one's balance, such as exercise, strength, sleep, cognitive functioning, vitamin D supplements, and medication management. Lifestyle changes to improve balance will take time to build up their protective effect. Measuring balance and fall risk affords the opportunity to detect subtle balance changes that can occur with health and lifestyle adjustments.

The human balance control system is very complex with three or more sensory inputs creating a repertoire of motor outputs, each with differing strategies that are affected by subconscious and conscious control, experience, context, and personality. The circumstances surrounding falling further complicates matters as the source of a fall can be from numerous intrinsic and extrinsic factors. Consequently, predicting falls with a basic measure of balance is insufficient on its own. The added insight and predictive power that machine learning techniques provide for human balance control systems can facilitate a more accurate prediction of falls.

One such machine learning approach is discussed in <CIT>. The '<NUM> patent uses Hidden Markov Model techniques for determining postural stability by identifying different postural states from center of pressure (COP) data. COP is the central location of combined pressure from <NUM> or more pressure or load sensors. The postural states relate to a classification of either static or dynamic. As the names suggest, a static postural state is defined as a dwell region within the COP data wherein sway is constrained to a single equilibrium. While a person is in a static state their body sway is considered under control and the person is more balanced and less likely to fall. A dynamic postural state is defined as sections of COP data that are not constrained to any equilibria and are by definition, unconstrained or uncontrolled. While a person is in a dynamic state they are considered to be "escaping" an equilibrium and are either moving to another equilibrium or falling. The static and dynamic postural states facilitate an assessment of postural stability undocumented before, defining a new model of postural control: the punctuated equilibrium model (PEM). The PEM is defined as periods of stability punctuated by dynamic trajectories. The PEM classification of postural states is particularly applicable for real-time or near-real-time assessment of stability. However, subsequent metrics that quantify the postural states facilitate a determination of instability trends along longer timelines. Measures of postural instability within the PEM are identified as: number of equilibria, equilibria dwell time and size of equilibria.

There are a number of advantages of the PEM approach. Firstly, the technique classifies otherwise uniform data, identifying stable regions and dynamic trajectories, with the latter being viewed as unstable. Threshold functions are described to identify the postural state users are in, whether for real-time identification or long term detection of postural instability. Further, the approach creates relative measures of stability that create independence from height and weight, location of the feet, or known stability boundaries.

While the preceding approach improved insights into postural stability, it is commonly understood that the multi-factorial nature of falls means that predicting falls outside of the real-time and near-real-time fall range is difficult to achieve. Despite the development to date, there remains a need for improved postural stability representation.

Determining a patient's fall risk remains a challenging task. Conventional fall risk indicators are whether an individual has previously fallen. Conventional fall risk assessment tests place the individual at risk, such as by placing the individual into challenging positions and gauging their stability in that position. However, a negative outcome of the test is a fall, and the test is thus not different from the conventional fall risk indicator. The PEM approach may be used for determining fall risk of a patient. Machine learning algorithms may be used to identify combinations of metrics and raw data that are indicative of an individual's fall risk. Because the PEM approach does not place the individual in a risky position, the individual's fall risk can be assessed with little danger to the individual.

According to embodiments of the disclosure, there is provided an improved method for balance and fall risk measurement and analysis that comprises the steps of acquiring load data points from at least two or more load sensors, calculating center of pressure (COP) for each data point, and using machine learning algorithms for classifying fall risk based on the calculated COP. The invention includes the Hidden Markov Model as the machine learning algorithm. The method may then include calculating the current postural state, the next postural state and a range of metrics. The metrics include at least one of the base punctuated equilibrium model (PEM) metrics, and at least one of a set of advanced PEM metrics: time to first equilibrium, equilibria distance, equilibria overlap, percent equilibrium, mean equilibria duration, and directional equilibria.

According to some embodiments of the disclosure, there is provided an improved method for balance and fall risk measurement and analysis that comprises the step of calculating the current postural state, the next postural state, and integrating a range of metrics. The metrics can include at least one of the base PEM metrics, and at least one of a set of advanced PEM metrics: time to first equilibrium, equilibria distance, equilibria overlap, percent equilibrium, mean equilibria duration, and directional equilibria, and at least one of the COP basic metrics. An integration of at least one metric from each of the base PEM metrics, advanced PEM metrics, and basic metrics can use one of several possible artificial intelligence techniques for determining the final balance score and fall risk. These approaches include: use of principal component analysis, Bayesian classification, neural network or deep-learning based strategies, and SVMs (support vector machines). In one embodiment, the integration model is a linear combination of stability metrics including at least one metric from each of the base PEM metrics, advanced PEM metrics, and basic metrics. The metrics are transformed to parameter scores on a scale of <NUM> to <NUM> and a composite balance score is calculated as a weighted average of the metrics. The range of the composite balance score may also be from <NUM> to <NUM>. Thresholds may be assigned to the balance score for classifying patients.

The determination of fall risk may be assisted, in some embodiments, by a system that houses load sensors as well as a signal preparation module that captures and transmits load data and, therefore, gathers equilibrium data about a person. The system may be a scale including two or more load sensors that wirelessly transmits load data to a mobile device and then to a data analysis module. In some embodiments, the scale may transmit data over a short-range communications link, such as Bluetooth or Wi-Fi, to the mobile device, such as a phone, tablet, or laptop computer, which then transmits the data over a long-range communications link, such as a wide area network (WAN) through the Internet to a server with a data analysis module. In some embodiments, the scale may transmit data over a short-range communications link to the mobile device, and the mobile device may include a data analysis module, and the results of the data analysis module may be uploaded to a server for monitoring and/or accessing the data. The data analysis module in a mobile device or server may perform processing of data, such as executing a machine learning algorithm and calculating the balance score and fall risk classification. In some embodiments, the results may be displayed on the system for display, such as with LEDs or an LCD on the scale.

In some embodiments, the system may be a device that houses two or more load sensors, the data analysis module, and a display for outputting the individual's balance and/or fall risk. The device may include a surface upon which an individual can stand comprising two or more load sensors. A data analysis module, such as a processor configured to perform steps for executing a machine learning algorithm, may process data from the two or more load sensors and generate balance information and/or a fall risk classification. Illuminating member of the device may comprise LED lights that illuminate through a semi-transparent top surface creating a glow effect of color that represents the fall risk classification of the user, and LED numbers illuminating through the top surface may display the balance score and weight. The surface device may also include a signal preparation module, which may transmit the load data, balance information, weight, and/or fall risk information to other equipment, such as a mobile device (e.g., a mobile phone, a tablet, a smart watch, a fitness watch, a fitness tracker, a laptop computer) or to a server. The signal preparation module may include communications equipment for communicating over either a short range communications link such as Wi-Fi or Bluetooth to transmit the data to another computer or the Internet or a long range communications link such as <NUM>, <NUM>, or <NUM> cellular communications.

In general, technology described in embodiments herein provides a system and method for determining a person's fall risk and/or composite balance score. The technology may be used, for example, by seniors, athletes, patients, doctors, physical therapists, nurses, astronauts, and/or any person that needs to assess fall risk or postural stability.

For a more complete understanding of the disclosed system and methods, reference is now made to the following descriptions taken in conjunction with the accompanying drawings.

In general, aspects of the present disclosure relate to methods and systems for determining a person's fall risk. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. This is in contrast to the general guidelines for preventing falls that are unrealistic in their expectation of increased vigilance and attention at all times. Alerting someone to their fall risk level empowers them to take action in the short term, such as to use a cane when the fall risk level is high, or for seeking professional advice for making lifestyle changes for long term improvement of fall risk. In some embodiments, data may be collected over days, weeks and/or months and long-term predictions formed for the individual.

<FIG> is a block diagram illustrating a conventional method of classifying postural states with a Hidden Markov Model (HMM). HMMs are temporal probabilistic models, modelling a series of states over time. These states are not directly observable, and thus are hidden. However, there is a set of possible observations at each point in time, which may correlate to the true hidden state at that time. Therefore, given a sequence of observations over a period of time, HMMs determine the most likely hidden state.

Conventionally, a HMM may classify postural states from center of pressure (COP) data. COP data may represent the central location of combined pressure from pressure or load sensors over a period of time and associated with a person. Pressure data is acquired from at least one pressure sensor over a period of time <NUM> and the COP is calculated for each pressure data point <NUM>. A HMM calculation determines the current and/or next postural state <NUM>. The HMM utilizes a set of probabilities for each postural state to determine the next postural state <NUM>. The postural states relate to a classification of either static or dynamic. The static postural state is defined as a dwell region within the COP data wherein sway is constrained to a single equilibrium. While a person is in a static state their body sway is considered under control and the person is more balanced and less likely to fall. A dynamic postural state is defined as sections of COP data that are not constrained to any equilibria and are by definition, unconstrained or uncontrolled. While a person is in a dynamic state they are considered to be "escaping" an equilibrium and are either moving to another equilibrium or falling.

The static and dynamic postural states facilitate a punctuated equilibrium model (PEM) of postural stability. The PEM is defined as periods of stability punctuated by dynamic trajectories. Alerting a person to that transient dynamic and thereby dangerous state can help them take instant action to avoid the imminent fall. Base measures of postural instability from the PEM <NUM> are identified as: number of equilibria <NUM>, equilibria dwell time <NUM>, and size of equilibria <NUM>. The number of equilibria <NUM> may include a number of equilibria identified in a time series. The dwell time <NUM> may include a size of a pentagon or other shape that represents the time spent in that particular equilibrium. The size of equilibria <NUM> may include an average (or other characteristic such as mean, maximum, or minimum) of each point in the equilibrium to the center of the corresponding equilibrium.

Although the base punctuated equilibrium model (PEM) stability metrics <NUM>, <NUM>, and <NUM> may be sufficient for determining postural states. Additional stability metrics may improve determination of postural states and/or allow for the determination of fall risk and/or classifying an individual's fall risk. Embodiments of the invention use machine learning techniques, such as to classify dynamic and static postural states for a PEM with HMM techniques, using advanced PEM stability metrics. The PEM defines multiple equilibria punctuated by dynamic trajectories of COP data series. The PEM approach creates defined regions and geometric patterns from COP data trajectories. For example, <FIG> is a block diagram illustrating a method for determining advanced PEM metrics, including time to equilibrium <NUM>, equilibrium distance <NUM>, equilibrium overlap <NUM>, percent equilibrium <NUM>, mean equilibria duration <NUM>, directional equilibria <NUM>.

In one embodiment of calculation of the advanced PEM metrics, data from at least two load sensors are acquired over a period of time at block <NUM> and associated with a person. The COP data may be calculated from the load sensor inputs for each load data point <NUM>. This may generate a time series of COP data. A HMM calculation may be used to determine a current and/or next postural state at block <NUM>. The HMM may use a set of probabilities for each postural state to determine a next postural state at block <NUM>. In some embodiments, the HMM calculation determines the next state, the current state, and/or one or more past states (e.g. five, ten). The postural states may relate to a classification of either static or dynamic. The static postural state may be defined as a dwell region within the COP data wherein sway is constrained to a single equilibrium. The classification of the time series for postural state may then allow calculations of base PEM stability metrics <NUM> as well as advanced PEM stability metrics <NUM>, including time to first equilibrium <NUM>, equilibria distance <NUM>, equilibria overlap <NUM>, percent equilibrium <NUM>, mean equilibria duration <NUM>, and directional equilibria <NUM>. In some embodiments, PEM stability metrics <NUM> may include time to first equilibrium (e.g., time elapsed before first equilibrium establishment), equilibria distance (e.g., mean distance of center of equilibria to adjacent equilibria centers), equilibria overlap (e.g., percentage of equilibria overlap of equilibria <NUM>% circle in a time series), percent equilibrium (e.g., percent of time spent in equilibrium in a time series), mean equilibria duration (e.g., mean duration of equilibria in a time series), and/or directional equilibria (e.g., weighted number of equilibria by the degree of anterior posterior deviation of the directional vector to adjacent equilibria centers from the medial lateral, X-axis). Additional details regarding the determining the COP data, determining postural states, and determining base PEM stability metrics are described in <CIT> and entitled "Determining postural stability,".

<FIG> is a block diagram illustrating a method for determining fall risk using a machine learning algorithm and (center of pressure) COP data according to some embodiments of the disclosure. A method for determining fall risk begins at block <NUM> with acquiring load data points from at least two load sensors over a period of time. Then, at block <NUM>, each load data point may be used to calculate center of pressure (COP) data. Next, at block <NUM>, machine learning algorithms may receive the COP data and calculate, for example, postural states. Then, at block <NUM>, the machine learning algorithms may be used to estimate fall risk and/or classify fall risk. In some embodiments, the machine learning algorithms may be used to classify postural states for calculating subsequent metrics and determine fall risk thresholds at block <NUM>. In other embodiments, the machine learning algorithm may be used to classify fall risk as the objective function, either with or without the preceding determination of postural states. In some embodiments, the estimated fall risk may also be based, in part, on at least one of clinical records, exercise, lifestyle inputs, weight, body fat composition, body mass index, level of hydration, medication consumption, alcohol consumption, sleep, steps per day, exercise, time spent sitting, and/or strength.

<FIG> is a block diagram illustrating a machine learning algorithm for determining balance score and fall risk classification based on data acquired from load sensors according to some embodiments of the disclosure. The COP is calculated at block <NUM> from the load data received at block <NUM> for each load data point over a period of time. The postural state classification at block <NUM> classifies two states: static and postural state with HMM techniques. The HMM may utilize a set of probabilities for each postural state to determine the next postural state at block <NUM>. A balance integration model may be determined at block <NUM> from the base PEM stability metrics calculated at block <NUM> and the advanced PEM stability metrics <NUM>. For example, a balance score and/or fall risk determination may be made based, in part, on a weighted combination of one or more base PEM stability metrics calculated at block <NUM> and one or more advanced PEM stability metrics calculated at block <NUM>. In some embodiments, the balance integration module of block <NUM> may also be based on basic postural stability metrics <NUM> from an inverted pendulum model (IPM) using one of several possible artificial intelligence techniques. A balance score and/or fall risk classifier may be generated at block <NUM> from the balance integration model of block <NUM>. Strategies for determining the final balance score include use of principal component analysis, Bayesian classification, neural network or deep-learning based strategies, SVMs (support vector machines), or supervised and unsupervised learning approaches more broadly. In addition to the stability metrics, raw data, such as COP values over time or load values over time, may also be provided to the artificial intelligence. In the case of a neural network, the network can be trained (using training data from individuals with a known fall history) to identify combinations of metrics and raw data indicative of fall risk.

In one embodiment, the balance integration model <NUM> may be a linear combination of stability metrics including: at least two of the basic PEM metrics <NUM> combined with at least two of the advanced PEM metrics <NUM> and at least two of the basic metrics <NUM> to create a robust representation. The selected metrics may be used to generate a score on a scale of <NUM> to <NUM>, and for some metrics a logistical function transformation may be necessary. Metrics are then weighted to optimize classification of fall risk, yielding a balance score at block <NUM>.

In some embodiments, the method may incorporate a number of input metrics from differing theoretical models. For example, one such model is the IPM that yields basic COP metrics <NUM> describing the sway around a single point. The metrics include anterior-posterior COP peak sway (e.g., maximum anterior-posterior displacement in a time series), mediolateral COP peak sway (e.g., maximum mediolateral displacement in a time series), standard deviation of mediolateral sway, standard deviation of anterior-posterior sway, the radius of a <NUM>% circle (e.g., radius of the circle that includes <NUM>% of the COP data in a time series) or ellipse (e.g., radius of the ellipse that includes <NUM>% of the COP data in a time series), mean speed of COP (e.g., mean of a COP speed in a time series), root mean squared speed (e.g., root mean square value of the COP speed in a time series), and percentage time above a predetermined speed (e.g., fraction of time series above <NUM>/s in a time series), standard deviation of mediolateral position in a time series (e.g., stdCopML), standard deviation of anterior-posterior position in a time series (e.g., stdCopAP).

<FIG> and <FIG> illustrate metrics from both postural stability models, IPM and PEM, respectively. The IPM yield more gross metrics of a single cluster, while the PEM yields finer metrics as these data have been further classified to multiple clusters.

<FIG> is a stabilogram of center of pressure (COP) data classified into punctuated equilibrium with Hidden Markov Model techniques according to one embodiment of the disclosure as applied to one individual's data. The x axis is the COP mediolateral sway <NUM>, <NUM> is the left foot and <NUM> is the right foot. The y axis is the COP anterior-posterior sway <NUM> with <NUM> being anterior direction and <NUM> the posterior direction. The x and y axis relate to the distance of sway. The different color shades represent different defined equilibria with a pentagon <NUM> of matching color overlaying the clustered regions of static equilibrium. The size of the pentagon represents the relative size of that equilibrium <NUM>. The larger the pentagon the longer the person remained in control in that equilibrium. The line within the pentagon <NUM> represents the mean distance each COP point is from the equilibrium center of the equilibrium it is associated. The points with an outer black line represent points in a dynamic state <NUM> and thus, have no equilibrium or pentagon associated.

<FIG> is the stabilogram of <FIG> with the basic metrics of mediolateral peak sway (XSWAY) and anterior-posterior peak sway (YSWAY) overlaid. The x axis <NUM> and y axis <NUM> relate to the distance of sway. The peak anterior-posterior peak sway <NUM> is the distance between the maximum anterior and maximum posterior sway. Likewise, the mediolateral peak sway <NUM> is the distance between the maximum sway points in the mediolateral direction. These metrics represent the deviation around the central point, and how far the sway deviates from the center.

Similarly, <FIG> is a graph illustrating data of a stabilogram of center of pressure (COP) data classified by a punctuated equilibrium model (PEM) according to one embodiment of the disclosure. <FIG> shows the same stabilogram of center of pressure (COP) data as <FIG> represented by the single equilibrium model of postural stability with the <NUM>% ellipse identified according to one embodiment of the disclosure. In <FIG>, the pentagons have been removed, but the classified regions are clearly indicated by their differing shades of grey <NUM>. This is in contrast with <FIG>, which illustrates the IPM uniform representation of the time series <NUM>. The <NUM>% ellipse contains <NUM>% of all of the data points and is a representation of postural stability by the total sway area <NUM>. Visually, it is clear to see the HMM classification provides different elements of the stabilogram. Together the metrics from both of these two models: the IPM and the HMM, may provide a more robust and comprehensive approach that neither may create in isolation.

<FIG> is a block diagram illustrating a balance score with three fall risk classifications according to one embodiment of the disclosure. The composite balance score <NUM> has a range from <NUM> to <NUM>. <NUM> is the best balance, and <NUM> is the least stable. Thresholds for fall risk can be identified at block <NUM> and in <FIG> they are defined as high risk for falling <NUM> if the person scores <NUM>-<NUM>, moderate risk for falling <NUM> if the person scores <NUM>-<NUM> and low risk for falling <NUM> if the person scores <NUM>-<NUM>. The thresholds can also be based, in part, on injury state, mental state, cognitive state, medical state, movement state, health state, attention state, intoxicated state, and/or hypoxia state.

<FIG> is a graph showing data of a Receiver Operating Curve for identifying falls according to one embodiment of the disclosure. The curve provides an evaluation of fall risk classification. These data are based on <NUM> subjects, with a mean age <NUM> years, and their fall history within a year. The y axis represents the accumulative true positive identification of a fall occurrence <NUM>, and the x axis represents the accumulative identification of no falls <NUM>. Points <NUM> through <NUM> on the PE line <NUM> represent each possible score of the composite balance score <NUM>. The line of no effect <NUM> depicts the theoretical location of equal levels of positive and negative identification, and thereby having no discrimination capability. A clear threshold for maximizing high fall risk classification <NUM> occurs at score <NUM><NUM>. The accumulation of score <NUM>, <NUM>, and <NUM> yields a sensitivity for correctly classifying those at risk for falling as <NUM>%, with a false positive rate of <NUM>%, <NUM>% specificity. The rate of identifying falls is minimal from <NUM> onwards <NUM> and therefore, classifies the upper, low fall risk range <NUM>, as illustrated in <FIG>.

Furthermore, the classified output can be sensitive to subtle changes in balance created by lifestyle factors. <FIG> is a graph showing data of an individual person's balance score and fall risk data over two years with annotated balance influencers according to one embodiment of the disclosure. Notable periods of increased and reduced balance and fall risk are related to the participant's activity. A fall event occurred after the participant scored low and correctly identified as being at high risk for falling <NUM>. Physical therapy (PT) was prescribed after the fall event and coincided with an elevation of the balance score and reduction of fall risk <NUM>. The trend is maintained with specific balance classes offered in the participant's associated living facility. The end of those classes and a reduction in exercise during the New Year was associated with a lower score and greater fall risk. These data depict the value of this invention for quantifying subtle changes in fall risk and empowering people to be proactive about their health.

A system may be used for determining postural stability and fall risk for a person. The system may include components for capturing load data, processing the data as necessary, transmitting the processed data, performing additional processing of the data based on a plurality of balance-related metrics to present balance and fall risk data for the person in question, transmitting data results, and displaying the data to the user, third party provider, and/or other support personnel to advise the reader of the person's postural stability and fall risk.

<FIG> is a block diagram illustrating a system and data flow throughout the system according to one embodiment of the disclosure. The system includes two or more load sensors <NUM> that collect load data for a period of time. The system may also include a signal preparation module <NUM> housed within a balance device <NUM> with wireless transmission capability for transmitting the load data <NUM> to a communication module <NUM> and, according to one aspect of the present invention, then to a cloud-based data analysis module <NUM>. The signal preparation module <NUM> may contain analog-to-digital converters (ADCs), timers, and other discrete or integrated components used to convert the output of the load sensor module(s) <NUM> to digital data values. The signal preparation module <NUM> may include any general purpose processor, a microprocessor, amplifier, other suitably configured discrete or integrated circuit elements, and memory. The memory may be any type of volatile or non-volatile storage medium including solid-state devices such as DRAM, SRAM, FLASH, MRAM or similar components for data storage. The signal preparation module <NUM> may be configured with circuitry and/or instructions to process data from the load sensors (e.g., convert analog to digital or otherwise interpret the load sensor signals) and/or package the data for transmission over a network connection or other bus (either wired or wireless), such as by forming packets or frames for network transmission or assembling data for USB transfer. A power source such as a battery (not shown) may be attached by any suitable arrangement for providing power to the circuits of the load detecting module <NUM> and signal preparation module <NUM>.

In one embodiment, the communication module <NUM> may comprise one or more integrated circuits (e.g. microcontroller, etc.) and/or discrete components on a printed circuit board or other electronic packaging technology. For example, the communication module <NUM> may include a RF transceiver for transmitting and/or receiving data prepared by the signal preparation module <NUM>. The communication module <NUM> may transmit and receive data <NUM> over any type of communications link, for example, the communication module <NUM> may include a wireless transceiver utilizing an RF network such as a Bluetooth network. The communication module <NUM> may include authentication capability to limit transfer of data to only authorized devices. Additionally, the communication module <NUM> may encrypt data before transmission <NUM> in order to prevent unauthorized access to the information. In some embodiments, the communication module <NUM> may include a smartphone, smartwatch, tablet, or laptop that includes the ICs, components, and/or code described above.

The data analysis module <NUM> contains instructions that may be executed by a processor of the data analysis module <NUM>, which may be local or remote. In some embodiments, the data analysis module <NUM> may be coupled to the signal preparation module <NUM> to provide a single apparatus capable of processing and analyzing the COP data and displaying results. In some embodiments, the data analysis module <NUM> may be a laptop, desktop or, cloud-based machine, near or remote from an apparatus with the load sensors, such that the data analysis module <NUM> receives load sensor data from the communications module <NUM>. Even when the data analysis module <NUM> is receiving data from the signal preparation module <NUM>, a communication module <NUM> may still be present to relay results of the balance score and/or fall risk determination to a remote location, such as a medical provider.

The data analysis module <NUM> may include a processor programmed to receive the load data <NUM> or COP data <NUM> from the communication module <NUM>, which applies machine learning techniques <NUM> to determine balance score and fall risk information <NUM>. The machine learning techniques <NUM>, including HMM may be performed on a processor. Subsequently, the processor calculates the base PEM metrics <NUM> (e.g., metrics that involve capturing the presence of the postural states), advanced PEM metrics <NUM> (e.g., metrics that involve capturing how the postural states relate to each other in space and time), and basic stability metrics <NUM>. Advanced PEM metrics may be any metric other than the metrics <NUM>, <NUM>, <NUM>. The results may be stored locally in memory with the processor and then wirelessly transmitted <NUM> for display by display module <NUM> or other display or other storage for later retrieval. A computer program may implement or use the machine learning and balance integration algorithm <NUM> described in embodiments above when executed by the data analysis module <NUM>. The modules <NUM>, <NUM>, and <NUM> may be integrated in a single device, or split between two, three, or more devices.

<FIG> also illustrates an embodiment of the system and data transmission throughout the system. Load data is collected from two or more load sensors over a period of time. The collected data is processed using a processor to calculate COP. A processor implements a machine learning algorithm that calculates basic postural stability metrics <NUM> and PEM metrics based on HMM techniques, including base PEM stability metrics <NUM> and advanced PEM stability metrics <NUM>. The processor integrates these metrics to develop a balance output, a fall risk output or both. The data can be transmitted <NUM> along a hard-wired system or a wireless system. The signal preparation module <NUM>, communication module <NUM>, and data analysis module <NUM> and their associated processors can be located in the balance device <NUM>, or across additional devices, for example, a tablet and the cloud.

In one embodiment, the collected load data <NUM> may be first processed in the signal preparation module <NUM>. The load data <NUM> is then wirelessly transmitted <NUM> to a mobile device <NUM> and then to a cloud-based data analysis module <NUM>. These data are processed on a processor to calculate COP <NUM> and subsequently, basic postural stability metrics <NUM>, basic PEM stability metrics <NUM> and advanced PEM stability metrics <NUM>. The processor integrates these metrics <NUM> to determine fall risk and a single balance score <NUM>. The results are stored locally by the processor in memory and the results are wirelessly transmitted <NUM> to the mobile device <NUM> for display and storage, and further transmitted to the balance device <NUM> for display by display module <NUM>. Although the display module <NUM> is shown in the balance device <NUM>, the display module <NUM> may alternatively be located in another device of the system, such as a mobile device that includes the communication module <NUM> and communicates with the balance device <NUM>.

The balance device <NUM> can be any variety of load detecting balance and fall risk devices, including a scale, mat, floor panel, shoe, insole, sock, walker, cane, prosthetic or robotic leg. The communication module <NUM> can be any variety of a mobile device, smartwatch, smartphone, tablet, computer, cloud-based service and/or data analysis module. If the communication device <NUM> is a tablet, the user may hold the device or have it near the scale during the test, or attached to a wall in front of the user. <FIG> illustrates a perspective view of a balance scale <NUM> with a tablet as the communication module <NUM>, in accordance with one embodiment.

If the communication device <NUM> is a smartphone, the user may hold the device or have it near the scale during the test or attached to a wall in front of the user. <FIG> illustrates a perspective view of the balance device <NUM> with a smartphone as the communication module <NUM>, in accordance with another embodiment of the present invention. <FIG> illustrates a perspective view of the balance device <NUM> with a cloud-based data analysis module <NUM> as the communication module <NUM>, in accordance with yet another embodiment.

<FIG> is an exploded view illustrating a scale balance device according to one embodiment of the disclosure. In this embodiment there are <NUM> main layers: the top layer is glass <NUM> or another semi-transparent material, and the casing <NUM> is the bottom layer. The components of the load casing <NUM> are housed within the casing <NUM> and affix to the top layer <NUM>. The feet <NUM> extend through the casing holes <NUM>. There may be no external buttons or switches on the scale, but a display of numbers <NUM>, functioning as part of the display module <NUM>, may be housed within the casing <NUM>.

<FIG> is an exploded view illustrating a load detecting module according to one embodiment of the disclosure. The load detecting module <NUM> includes load casing <NUM>, a load cell <NUM> and foot <NUM>. The load cell <NUM> is embedded within the load casing <NUM>. The load casing <NUM> is affixed to the top glass layer <NUM>, and force is exerted through to the foot <NUM> enabling the load cell <NUM> to deform and detect load change, in accordance with one embodiment of the present invention.

<FIG> is a side perspective view illustrating a load detecting module according to one embodiment of the disclosure. <FIG> illustrates how the components of the load detecting module <NUM>: the foot <NUM>, load cell casing <NUM>, and load cell <NUM>, fit together.

<FIG> is a side perspective view illustrating a scale balance device according to one embodiment of the disclosure. <FIG> shows a rear perspective view of one version of the balance device. In this embodiment, the casing <NUM> is not completely matching the area of the top layer <NUM> but, instead, is a shaped casing <NUM> with partial coverage.

<FIG> is a plan view illustrating a scale balance device and display according to one embodiment of the disclosure. The display module <NUM> may include four <NUM>-segment LEDs <NUM> at least <NUM>" long, and a plurality of LED lights <NUM> throughout the casing to provide a glowing illumination effect through the semi-transparent top surface <NUM> of the balance device <NUM>. The glowing illumination provides an indication of fall risk determined at block <NUM> of <FIG>: red is high risk <NUM>, yellow is moderate risk <NUM>, and green is low risk <NUM> of falling, in accordance with one embodiment of the present invention. The size of the numbers <NUM> and illuminating the risk factors may be sized to provide the user their result without requiring the user to bend down to see the display <NUM>.

Standard materials, well known in scale construction can be used to make the scale. This may include plastic injection molding for the casing <NUM>, load casing <NUM>, and feet <NUM>, tempered glass for the top layer <NUM> that is made semi-transparent by film, etching, paint or any combination of those techniques.

In one embodiment, the balance measuring scale may be absent of any external buttons and switches so as to not require user inputs. The scale <NUM> also includes illuminating numbers <NUM>, preferably at least about two inches long (at least about <NUM> long), that illuminate a visual display <NUM> on a balance device <NUM> that is low profile and more narrow than the width of standard walker axles. Utilizing an array of metrics from two models of postural control creates a robust measurement system for balance and fall risk detection. The outcome of which is the capability to detect balance and fall risk during a safe testing procedure, standing with eyes open, with no disruptors or sensory manipulations. Furthermore, the composite balance score <NUM> may simplify highly complex analytics necessary to depict postural stability to a single balance score from <NUM> to <NUM> that is easily comprehended by a user. Altogether, this system provides seniors or any users the ability to test themselves unsupervised, without either a clinician or an assistant.

In use, a user would mount the scale <NUM> and adopt a comfortable standing position, keeping as still as possible. There may be a notification on the scale <NUM> and/or communication module <NUM> to indicate the test has commenced. In one embodiment, the test duration is <NUM> seconds. At the end of the test, there may be a notification sound and/or light to signify the test completion. The weight may be displayed on the scale <NUM> and/or a linked mobile device. Then, the balance score may be displayed <NUM> on the scale <NUM> and/or the linked mobile device. The fall risk may also be displayed <NUM> on the scale <NUM> and/or a linked mobile device, such as via an illuminated display <NUM> where color represents the risk classification.

Embodiments above describe the use of a machine learning algorithm and various metrics, such as basic PEM metrics and advanced PEM metrics, to estimate an individual's fall risk. Each individual metric, whether PEM or basic, has limited discriminatory power for detecting instability when viewed in isolation. For example, <FIG> show marginal or little difference in results between eyes open and eyes closed conditions for normal subjects for PEM metrics, such as the number of equilibrium shown in <FIG>, equilibrium dwell time shown in <FIG>, and the basic metric of <NUM>% confidence sway ellipse shown in <FIG>. Consequently, it was unexpected that the same metrics when combined with advanced PEM metrics (such as metrics that take into account the relationship in time and space between postural states generated by a HMM), correctly identified individuals who are at risk of falling in a study of <NUM> older adults that included self-testing. The advantage of the PEM analysis is that greater dynamism is detected so a safe, eyes open standing protocol can be used and self-testing is possible without placing the individual at risk. This is in contrast to most balance tests that challenge the balance of the subject to expose weaknesses, often requiring a clinician/operator to be ready to catch to treat an individual that falls.

If implemented in firmware and/or software, functions described above may be stored as one or more instructions or code on a computer-readable medium. Examples include non-transitory computer-readable media encoded with a data structure and computer-readable media encoded with a computer program. Computer-readable media includes physical computer storage media. A storage medium may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise random access memory (RAM), read-only memory (ROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc includes compact discs (CD), laser discs, optical discs, digital versatile discs (DVD), floppy disks and Blu-ray discs. Generally, disks reproduce data magnetically, and discs reproduce data optically.

In addition to storage on computer readable medium, instructions and/or data may be provided as signals on transmission media included in a communication apparatus. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the claims.

Claim 1:
A method of determining postural stability and fall risk of a person, comprising:
receiving (<NUM>), by a processor, a plurality of load data points over a period of time from at least two load detecting modules; and
estimating, by the processor, a fall risk by applying a machine learning algorithm to the plurality of load data points, wherein the step of estimating the fall risk comprises:
calculating (<NUM>) a time series of center of pressure (COP) data based on the plurality of load data points;
determining (<NUM>) a plurality of posture states identified with Hidden Markov Model techniques based on the time series of center of pressure (COP) data; and
calculating (<NUM>) one or more first stability metrics of a punctuated equilibrium model of postural stability, the punctuated equilibrium model (PEM) describing posture states as periods of postural stability, equilibria, and dynamic postural trajectories between the periods of postural stability, wherein the first stability metrics are based on a PEM assessment of the plurality of the determined posture states and comprise one or more of:
number of equilibria,
equilibria dwell times, and
sizes of equilibria, each size of an equilibrium including a characteristic of distances of points in the equilibria from the center of the equilibrium;
characterized in that the step of estimating the fall risk further comprises:
calculating (<NUM>) one or more second stability metrics of the punctuated equilibrium model, the second stability metrics based on the PEM assessment of the plurality of the determined posture states, wherein the second stability metrics comprise one or more of time to first equilibrium, distances between equilibria, equilibria overlap, percentage of time in equilibrium, mean equilibria duration, and directional equilibria based on weighting of equilibria by a degree of anterior posterior deviation from a medial lateral axis of the person of a directional vector to adjacent equilibria centers; and
determining the fall risk based on the one or more first stability metrics and on the one or more second stability metrics.