Patent Description:
Warehouse and manufacturing users perform various physical and/or repetitive tasks. Such physical tasks can include lifting and/or holding relatively heavy objects for an extended period of time and/or operations that require numerous repetitive motions (e.g., manually sanding a structure by moving a sanding tool in a circular direction a repeated number of times). Performing a physical task can sometimes result in high strain activity.

Document<NPL>, according to its abstract, relates to LASEC, the first technique for instant do-it-yourself fabrication of circuits with custom stretchability on a conventional laser cutter arid in a single pass. The approach is based on integrated cutting and ablation of a two-layer material using parametric design patterns. These patterns enable the designer to customize the desired stretchability of the circuit, to combine stretchable with nori-stretchable areas, or to integrate areas of different, stretchability. For adding circuits on such stretchable cut patterns, we contribute routing strategies and a real-time routing algorithm. An interactive design tool assists designers by automatically generating patterns and circuits from a high-level specification of the desired interface. The approach is compatible with off-the-shelf materials and can realize transparent interfaces. Several application examples demonstrate the versatility of the novel technique for applications in wearable computing, interactive textiles, and stretchable input devices.

Document <NPL>, according to its abstract, states that an auxetic design is proposed for the flexible membrane of a piezoelectric pulse sensor and computationally analyzed for a high-sensitivity vibration sensing in micro electro-mechanical system (MEMS). Auxetics are metamaterial structures with negative Poisson's ratio which enables sensor's flexible diaphragm to be expanded in both longitudinal and transverse directions easily. The sensitivity of a pulse sensor with an auxetic membrane was studied and compared to an equivalent plain membrane when the substrate was under harmonic bending. The sensing response was determined for the both models using detailed Finite Element Model (FEM) simulations. The sensor with the auxetic membrane demonstrated excellent sensitivity output over a harmonic pressure input which shows its strong potential for high-sensitive MEMS sensing applications. A detailed fabrication process is also discussed.

Document <NPL>, according to its abstract, states that Flexible and highly sensitive piezoresistive nanocomposites have been demonstrated to possess considerable potential for monitoring structural integrity and human physiological performance. To enhance the mechanical and strain sensing properties of these nanocomposites, different nanofillers (e.g., metal nanovvires, carbon nanotube, and graphene) have been incorporated in polymeric matrices to establish electrically conductive pathways that are also sensitive to applied strains. Their piezoresistivity mainly stem from nanofillers' intrinsic piezoresistivity, tunneling effect, and contact resistance changes of the nanofiller networks. Although many high-performance nanocomposite strain sensors have been developed and using different techniques, the empirically guided fabrication approach can be laborious, inefficient, and, most importantly, unpredictable. Therefore, this study proposes a topological design-based approach to strategically control and manipulate the strain sensing performance of the nanocomposites, simply by altering its geometric pattern design. First, polyethylene terephthalate (PET) substrates were patterned with pre-designed hierarchical inhomogeneous topologies and kirigami cuts created using a laser cutter. Second, the substrates were spray-coated using a carbon nanotube (CNT)-latex to deposit the strain-sensitive thin films. Third, the strain sensing performance of the CNT-latex nanocomposite thin films of different topologies was characterized and compared. It was found that, as the initial solid mechanics analysis predicted, the hierarchical inhomogeneous topology effectively enhanced the nanocomposites' strain sensitivity, while the kirigami cuts significantly reduced sensitivity. The proposed methodology can help guide the development of high-performance nanocomposites with preprogrammed sensing properties for structural and human health monitoring applications.

Document <CIT>, according to its abstract, states that a miniaturized, ruggedized, field-deployable Portable Exposure Assessment System (PEAS) is used to remotely monitor workers and provide real-time warning of exposure to musculoskeletal injury conditions via alarm and smart-phone transmission. The PEAS unit wirelessly acquires exposure data from sensors; conducts initial data analysis; triggers proximal and remote alarms; sends out text messages with abnormal data, GPS locations, and time stamps to a safety office; and saves data for more extensive assessment. Sensor technology is used in this field-deployable system to simultaneously measure and collect the body loads and awkward postures imposed by package handling as well as driving-related, low-frequency vibration exposures. Wireless technology is used to set up wireless communication links between the sensors and a data logger and between the data logger and a smart phone with GPS, date/time stamp and text messaging capabilities.

According to the present disclosure, a method, a device and a system as defined in the independent claims are provided. Further embodiments of the claimed invention are defined in the dependent claims. Although the claimed invention is only defined by the claims, the below embodiments, examples, and aspects are present for aiding in understanding the background and advantages of the claimed invention.

The figures are not to scale. Instead, the thickness of the layers or regions may be enlarged in the drawings. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. As used in this patent, stating that any part (e.g., a layer, film, area, region, or plate) is in any way on (e.g., positioned on, located on, disposed on, or formed on, etc.) another part, indicates that the referenced part is either in contact with the other part, or that the referenced part is above the other part with one or more intermediate part(s) located therebetween. Connection references (e.g., attached, coupled, connected, and joined) are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and in fixed relation to each other. Stating that any part is in "contact" with another part means that there is no intermediate part between the two parts. Although the figures show layers and regions with clean lines and boundaries, some or all of these lines and/or boundaries may be idealized. In reality, the boundaries and/or lines may be unobservable, blended, and/or irregular.

Manufacturing operations often necessitate users to perform various types of repetitive physical tasks and/or lift objects that are relatively heavy. Performing repetitive physical tasks during certain manufacturing operations can cause undesired risk to users performing such repetitive physical tasks. For example, performing physical tasks repetitively can result in muscle and/or tendon fatigue over time. Muscle fatigue can reduce a strength of a muscle and/or tendon fatigue can reduce structural capacity of a tendon.

To improve ergonomic awareness, ergonomics improvement systems have been developed to monitor and/or quantify musculoskeletal performance during repeated performance of a physical task or manufacturing operation. Generally, existing technologies are focused on gathering posture and/or movement information for treating injuries. For instance, some known systems monitor musculoskeletal performance using sensors to capture data during a repetitive motion. One known system simulation of a person performing the physical tasks over a number of cycles is run by a computer system using the musculoskeletal model for the person and at least one of the task performance data and task description data. The computer simulated model can be used to track motion and/or analyze the detected motion. To capture data for use with a simulated model, some known ergonomics improvement systems employ one or more sensors. The sensors can sense force and/or motion. However, the sensors of these known ergonomics improvement systems do not detect or sense stress and/or strain applied to one or more joints (e.g., a shoulder joint, an elbow joint, a wrist joint, etc.) of an user performing physical tasks.

Example ergonomics improvement systems disclosed herein employ movement, load measurement and/or feet positioning to determine stress and/or strain that a limb, a joint of a limb, and/or a body is undergoing when an user is performing one or more tasks (e.g., physical tasks involving repetitive motion). To track movement of a limb and/or detect stress and/or strain that a joint of a limb is undergoing when a user performs repetitive physical tasks, example ergonomics improvement systems disclosed herein employ one or more wearable sensors. Example wearable sensors disclosed herein, in combination with the ergonomics improvement system, provide a tracking system to track movement of a limb. In some examples, wearable sensors disclosed herein can include example upper body sensor systems, lower body sensor systems, and/or a combination of upper and lower body sensor systems. Data from example wearable sensors disclosed herein (e.g., upper body sensor systems and/or example lower body sensor systems) can be used (e.g.. , in aggregate or in isolation) to measure one or more of a position of a limb relative to a body, movement of an entire limb relative to a body, stress and/or strain that a joint of a limb is undergoing and/or any other movement(s) or angle(s) of a limb, body portion (e.g., upper back, lower back, etc.) and/or joint relative to a body.

Example wearable sensors disclosed herein include wearable sensors formed from one or more membranes (e.g., a meta-membrane(s)). The membrane(s) can be one or more appliques or patches that can be attached to garments, can be formed as a garment (e.g., a shirt), and/or be a part of garments (e.g., sleeve, etc.). In some examples, wearable sensors disclosed herein include example membrane(s) having Kirigami patterns. In some examples, wearable sensors disclosed herein include example membrane(s) having Auxetic patterns. Kirigami patterns and/or Auxetic patterns provide varying (e.g., increased) flexibility to enable the sensors to accommodate a larger range of motion (e.g., compared to other patterns and/or sensors having a solid surface without patterns). In some examples, the Kirigami and/or Auxetic patterns can be more durable and/or resistant to cracking over time. However, the technical advantages are not limited to these examples. In some examples, wearable sensors disclosed herein can include any other type of meta-membrane(s) and/or membranes having other patterns. For instance, ergonomics improvement systems disclosed herein can employ different types of wearable sensors and/or meta-membrane(s) (e.g., Kirigami, bi-axial Kirigami, Auxetic hexagonal, etc.) that can output signals that can be used to track limb movement, stress, strain, and/or obtain limb position data. In some examples, example wearable sensors disclosed herein can couple (e.g., be attached) to one or more limbs of a body and/or can be positioned across one or more joints to measure stress and/or strain imparted to a limb of a body. For example, the wearable sensors disclosed herein can be attached to an arm of a user to detect stress on a shoulder, elbow and/or a wrist of a user. In some examples, the wearable sensors can be attached to a leg, a hip, a knee, an upper back, and/or a lower back of a user to detect stress and/or strain at a knee, hip, neck, upper back, and/or lower back, etc. In some examples, the wearable sensors can be employed or coupled proximate each joint of a limb to measure or detect a position and/or joint angle of a joint (e.g., a shoulder joint, a wrist joint, etc.) associated with the wearable sensors.

To measure a load carried by a user and detect feet positioning of a user, example ergonomics improvement systems disclosed herein employ the lower body sensor system. To measure load, example lower body sensor systems disclosed herein can employ load cell, a pressure sensor, and/or any other sensor(s) for measuring load and/or weight. To detect feet positioning during physical tasks, example lower body sensor systems disclosed herein can employ Lidar sensors, pressure pads and/or pressure scan sensors, and/or any other suitable positioning sensor(s). Example lower body sensor systems disclosed herein can be carried and/or housed by footwear (e.g., shoes, work boots, etc.) to be worn by a user performing physical tasks. In some examples, example lower body sensors disclosed herein can be placed on and/or within the sole of the footwear. Data from example lower body sensors disclosed herein can be used in aggregate with data collected from example upper body sensor systems disclosed herein to determine movement and/or a position of a limb. However, in some examples, ergonomics improvement systems disclosed herein can employ example upper body sensor systems disclosed herein without example lower body sensor systems disclosed herein to detect a position of a limb relative to a body and/or a joint angle of a joint.

To process data from example wearable sensors disclosed herein (e.g., example upper body and lower body sensor systems), example ergonomics improvement systems disclosed herein employ a controller. In operation, for example, an example controller disclosed herein can receive outputs from the wearable sensors. In some examples, an example controller disclosed herein can compare data from example wearable sensors to a user baseline threshold. For example, the baseline can be pre-determined values based on a first condition and a second condition of the user. For example, the first condition can be an amount of load carried by the person and the second condition can be a stance position of a user's feet when detected carrying the load. For example, a baseline threshold for a person carrying a fifty pound (i.e. approximatively <NUM>) weight while standing in a brace position (e.g., the user's feet are in a brace position as shown in <FIG>) will not exceed the baseline threshold. However, the baseline threswhold can be exceeded in response to detecting that the user is carrying a fifty pound weight while the user's feet are in a non-brace position (e.g., see <FIG>). In some examples, in response to determining that the data of the wearable sensors exceeds the user baseline threshold, example controllers disclosed herein can activate an alarm. Example alarms disclosed herein include, but are not limited to, visual alarms (e.g., a light), audio alarms (e.g., a speaker), haptic feedback (e.g., a vibration), a combination thereof and/or any other alarm(s). In some examples, the type of alarm(s) can be selected based on an environment (e.g., industrial or manufacturing environment) of the user. For example, where the environment can be noisy, busy, or where the tasks being performed should not be interrupted by abrupt or startling alarms, the type of alarm chosen (e.g., haptic feedback) can vary between the options discussed above and/or other types of alarms.

In some examples, example controllers disclosed herein compile outputs from the wearable sensors and transmit the data to a central processing system remotely located from the controller and/or the user. In some such examples, the example central processing system aggregates the data received from the controller and compares the data to a user baseline threshold. In response to determining that the data from the wearable sensors exceeds the user baseline threshold, the example central processing system instructs (e.g., sends a warning signal to) the controller to initiate the example alarm. To provide power to the controller and/or the wearable devices, the example ergonomics improvement system disclosed herein employs a power source. In some examples, an example power source can include a battery. In some examples, an example power source can include smart cloths and/or other devices that generate electricity. As used herein, the term "smart cloths" can include motion-powered fabric(s), fabrics that include integrated circuits that can generate power from sweat and/or friction (e.g., movement), frictional forms of human bio-energy, and/or any other fabric or device for generating energy to power one or more of the wearable devices and/or a controller (e.g., fabric piezoelectric nanogenerators that harvest human mechanical motion to energy).

Examples ergonomics improvement systems disclosed herein can track movement of an upper body (e.g., a shoulder, an elbow, a wrist/hand, a forearm, a lower back, etc.) and/or movement of a lower body (e.g., a hip, a knee, a foot, etc.). For example, to track a movement of a leg, one or more example wearable sensors (e.g., meta-membrane(s)) can be attached to (e.g., skin, clothing) a hip joint, a knee joint, an ankle joint, a lower back, an ankle joint, etc. In some examples, ergonomics improvement systems disclosed herein can track movement of a leg, an arm, a leg and an arm, both arms, both legs, both arms and both legs, an upper back, a lower back, and/or any other limb or portions of a body (e.g., a neck, a lower back, an upper back, etc.) to determine stress and/or strain that a body undergoes when a user performs physical tasks and/or activities.

<FIG> is an example ergonomics improvement system <NUM> in accordance with teachings disclosed herein. The ergonomics improvement system <NUM> of the illustrated example can detect strain and/or stress that a body undergoes when performing specific work tasks that include repetitive physical tasks. To detect strain and/or stress to a body (e.g., or a joint of a body), the ergonomics improvement system <NUM> of the illustrated example tracks and/or otherwise detects movement of a limb <NUM> (e.g., an arm 102a) and/or a joint (e.g., a joint angle, a shoulder joint <NUM>, a wrist joint <NUM>, an elbow joint <NUM>) of the limb <NUM> relative to a body <NUM> (e.g., a torso of a body).

The ergonomics improvement system <NUM> of the illustrated example includes an example controller <NUM>, an example limb sensor <NUM>, an example load sensor <NUM>, an example position sensor <NUM>, an example warning device <NUM>, and an example power device <NUM>. The limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, and the warning device <NUM> are communicatively coupled to the controller <NUM> via, for example, a bus, a physical wire, wireless communication protocol, Bluetooth and/or any other suitable communication protocol(s).

To track and/or detect movement of the limb <NUM> and/or the joint, the ergonomics improvement system <NUM> of the illustrated example employs the limb sensor <NUM> (e.g., a tracking system or an upper body sensor). The limb sensor <NUM> of <FIG> is a tracking system that can be coupled (e.g., directly attached) to the limb <NUM> and/or the joint of the body <NUM> and/or attached to clothing of the user 106a to obtain data associated with movement of the limb <NUM> and/or the joint when a user is performing one or more physical tasks (e.g., physical tasks involving repetitive motion). The ergonomics improvement system <NUM> includes the limb sensor <NUM>, also called a meta-membrane system or sensor, to couple to the limb <NUM> of the body <NUM>, and generates first outputs in response to movement of the limb <NUM> relative to the body <NUM> that are used to determine a position (e.g., an angular and/or rotational position) of the limb <NUM> relative to the body <NUM>. In the illustrated example, the limb sensor <NUM> is an upper body sensor system 111a that is attached to the arm 102a of the body <NUM>. However, in other examples, the limb sensor <NUM> can couple to a leg, a shoulder joint <NUM>, a wrist joint <NUM>, an elbow joint <NUM>, a knee joint, a hip joint, a lower back and/or any other portion of the body <NUM>. For example, the limb sensor <NUM> can be coupled or attached to the arm 102a, a leg, a hip, a knee neck, a lower back portion, an upper back portion and/or any combination thereof to track movement of one or more limbs and/or joints of a body <NUM> when the user 106a is performing physical activity. In some examples, multiple limb sensors <NUM> (e.g., tracking systems, upper body sensors, etc.) can be used to detect movement of multiple limbs or joints of the body <NUM> when the user 106a is performing a physical activity.

To detect and/or measure a load of the body <NUM>, the ergonomics improvement system <NUM> of the illustrated example includes the load sensor <NUM>. The load sensor <NUM> is to generate a second output representative of a load carried by the body <NUM>. The load sensor <NUM> of <FIG> can be a load cell, a pressure sensor, a pressure pad and/or any other sensor(s) for measuring load and/or weight of the body <NUM>.

To detect and/or otherwise determine a stance (e.g., feet positioning) of the user 106a performing a physical task, the ergonomics improvement system <NUM> of <FIG> employs the position sensor <NUM>. The position sensor <NUM> is to generate a third output representative of a position of a right foot of the body relative to a position of a left foot of the body. The position sensor <NUM> of <FIG> can detect and/or otherwise determine if a user is standing in a stable or bracing position (e.g., with one foot spaced apart and in front of their other foot) or a non-stable or non-bracing position (e.g., the user 106a standing with their feet spaced apart, but the left foot substantially in-line with the right foot) when performing the physical task(s). In some examples, by determining the position of each foot of the user 106a via the position sensor <NUM>, the ergonomics improvement system <NUM> of <FIG> can determine if the user's stance is stable or optimal for carrying a detected load (e.g., an object <NUM> (e.g., a box)). The load sensor <NUM> and the position sensor <NUM> of the illustrated example provide a lower body sensor system 111b of the ergonomics improvement system <NUM>.

To determine stress and/or strain that the limb <NUM> (e.g., a human limb), the joint, and/or the body <NUM> (e.g., an upper back, a lower back, etc.) undergoes during a physical task, the ergonomics improvement system <NUM> includes the controller <NUM>. The controller <NUM> of <FIG> is configured to determine whether one or more physical tasks or actions performed by the user 106a if performed with a less desirable or improper motion based on one or more limb sensor outputs <NUM>, load sensor outputs <NUM>, and/or position sensor outputs <NUM> received by the controller <NUM>.

To warn the user 106a when the controller <NUM> determines that detected improper or less desirable movement (e.g., non-ergonomic movement) of the user 106a, the ergonomics improvement system <NUM> of the illustrated example employs the warning device <NUM>. Based on the data provided by the limb sensor <NUM>, the load sensor <NUM> and/or the position sensor <NUM> to the controller <NUM>, the controller <NUM> controls an operation of the warning device <NUM> (e.g., via a warning signal <NUM>). The warning device <NUM> of the illustrated example can include, but is not limited to, a light, an audible alarm, haptic feedback and/or any other alarm(s). The warning device <NUM> can be carried by the controller <NUM> (e.g., a housing of the controller <NUM>), a clothing of the user 106a, attached to the body <NUM>, can be carried or integrated with footwear worn by the user 106a, and/or can be carried by a work hat, gloves, and/or any other tool that can be used by the user 106a.

Alternatively, in some examples, the controller <NUM> of <FIG> can be configured to receive the one or more limb sensor outputs <NUM>, the load sensor outputs <NUM> and/or the position sensor outputs <NUM> and transmit or communicate the data (e.g., via transmitter) to a remote location (e.g., a remote server, a central processing computer, a control room, etc.). A computer at a remote location can processes the data provided by the limb sensor <NUM>, the load sensor <NUM>, and/or the position sensor <NUM> to determine if the data represents user activity that exceeds an activity threshold. The remote computer can then communicate (e.g., send) instructions to the controller <NUM> to activate the warning device <NUM> if the remote computer determines that the activity exceeds a threshold.

To provide power to the controller <NUM> and/or the wearable devices or sensors, the example ergonomics improvement system <NUM> disclosed herein employs the power device <NUM> (e.g., a power source). The power device <NUM> of <FIG> provides power to the controller <NUM>, the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, and/or the warning device <NUM>. In some examples, the power device <NUM> provides power only to the controller <NUM> and/or the warning device <NUM>. For example, the controller <NUM>, the power device <NUM>, the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM> and the warning device <NUM> can be electrically coupled via one or more electrical wires. In some examples, the limb sensor <NUM>, the load sensor <NUM>, and the position sensor <NUM> are powered with dedicated power devices (e.g., batteries) independent from the power device <NUM> and/or the controller <NUM>. In some examples, the limb sensor <NUM>, the load sensor <NUM>, and/or the position sensor <NUM> are powered indirectly by the power device <NUM> through connection(s) with the controller <NUM>. For example, the power device <NUM> (e.g., a battery) can be electrically coupled with (e.g., to provide power to) the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, the controller <NUM> and/or the warning device <NUM>. In some examples, the limb sensor <NUM>, the load sensor <NUM> and the position sensor <NUM> have dedicated batteries and do not require power from the power device <NUM>.

The power device <NUM> of the illustrated example is a battery. In some examples, the power device <NUM> can include smart cloths and/or other device(s) that generate electricity. As used herein, the term "smart cloths" can include motion-powered fabric(s), fabrics that include integrated circuits that can generate power from sweat and/or frictional movement, frictional forms of human bio-energy, and/or any other fabric or device for generating energy to power the ergonomics improvement system <NUM> (e.g., one or more of the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, the warning device <NUM> and/or a controller <NUM>).

<FIG> is a perspective, enlarged view of the limb sensor <NUM> (e.g., the upper body sensor system 111a) of the example ergonomics improvement system <NUM> of <FIG>. The limb sensor <NUM> of the illustrated example is a wearable membrane that couples (e.g., attaches) to the arm 102a (or limb) of the body <NUM>. In the illustrated example, the limb sensor <NUM> includes a plurality of membrane sensors <NUM> that generate first outputs to track movement of the limb <NUM> or arm 102a.

The membrane sensors <NUM> of the illustrated example of <FIG> include a first membrane sensor <NUM> (e.g., a first membrane assembly), second membrane sensor <NUM> (e.g., a second membrane assembly), and a third membrane sensor <NUM> (e.g., a third membrane assembly). In the illustrated example of <FIG>, the first membrane sensor <NUM> (e.g., a shoulder membrane sensor system) is coupled adjacent or proximate a shoulder <NUM>, the second membrane sensor <NUM> (e.g., an elbow membrane sensor) is coupled adjacent or proximate an elbow <NUM>, and the third membrane sensor <NUM> (e.g., hand membrane sensor) is coupled adjacent or proximate a wrist <NUM>.

Each of the membrane sensors <NUM> detects movement of the user 106a and obtains (e.g., measure or calculate) movement data. For example, the limb sensor <NUM> of <FIG> includes the first membrane sensor <NUM> positioned proximate the shoulder <NUM> to generate first ones of first outputs (e.g., the limb sensor outputs <NUM>) in response to movement of the shoulder <NUM> that can be used to detect a position of the shoulder <NUM> relative to the body <NUM>. For example, the limb sensor <NUM> of <FIG> includes the second membrane sensor <NUM> positioned proximate the elbow <NUM> to generate first ones of second outputs (e.g., the limb sensor outputs <NUM>) in response to movement of the elbow <NUM> that can be used to detect a position of the elbow <NUM> relative to the body <NUM>. For example, the limb sensor <NUM> of <FIG> includes the third membrane sensor <NUM> positioned proximate the elbow <NUM> to generate first ones of third outputs (e.g., the limb sensor outputs <NUM>) in response to movement of the hand/wrist <NUM> that can be used to detect a position of the hand/wrist <NUM> relative to the body <NUM>.

Although the limb sensor <NUM> of <FIG> includes the membrane sensors <NUM>, in some examples, the limb sensor <NUM> can include only one sensor assembly (e.g., the first membrane sensor <NUM>), two membrane sensors, more than three membrane sensors, and/or any other number of membrane sensors <NUM>.

In some examples, the membrane sensors <NUM> can be implemented on cloth, woven fabric, or other material or apparel that can be worn by the user 106a. Additionally, each of the membrane sensors <NUM> of the illustrated example are formed as pads or patches that attach to a limb <NUM> and/or clothing of the user 106a. For example, the membrane sensors <NUM> can be attached to a sleeve or wearable device that can be removably worn by the user 106a. In some examples, each of the membrane sensors <NUM> of the illustrated example can include releasable fasteners such as, for example, a hook and loop fastener, Velcro® brand fasterner, straps and/or any other releasable fastener that can secure the membrane sensors <NUM> to the limb <NUM> of the body <NUM>. In some examples, membrane sensors <NUM> can be formed as a unitary membrane or wearable device that can be worn by the user 106a. For instance, the membrane sensors <NUM> can be formed as a sleeve or as shirt (e.g., an entire shirt composed of a membrane sensor) or other clothing that can be worn by the user 106a. In other words, instead of the first membrane sensor <NUM>, the second membrane sensor <NUM>, and the third membrane sensor <NUM>, an example limb sensor <NUM> can include a shirt that is formed of a unitary membrane sensor. In other words, the entire shirt can be a sensor and/or include sensor functionality. In some examples, the membrane sensor can be formed as a wearable device that can include, but is not limited to, a sleeve, a shirt, an attachable cloth, a sleeve, a rubber or flexible sleeve and/or any other wearable device or clothing. The membrane sensors <NUM> can be permanently attached to the cloth or piece of apparel and/or it can be removal and reattachable. In other examples, the membrane sensors <NUM> are directly attached to the arm 102a of the user 106a via removable adhesive, tape, etc..

To couple (e.g., communicatively and/or electrically) the membrane sensors <NUM>, the controller <NUM>, the warning device <NUM> and/or the power device <NUM>, the ergonomics improvement system <NUM> of <FIG> and <FIG> includes one or more wires <NUM> (e.g., an electrical wire). For example, the membrane sensors <NUM>, the controller, the warning device <NUM> and/or the power device are electrically coupled in series. For instance, the third membrane sensor <NUM> is connected electrically to the second membrane sensor <NUM> via a first wire 203a, the second membrane sensor <NUM> is connected electrically to the first membrane sensor <NUM> via a second wire 203b, and the first membrane sensor <NUM> is connected electrically to the warning device <NUM> via a third wire 203c. Alternatively, in some examples, the membrane sensor <NUM>, the controller <NUM>, the warning device <NUM> and/or the power device <NUM> can be communicatively coupled via wireless connection, a Bluetooth connection, and/or any other communication protocol. In some examples, the power device <NUM> provides power to the membrane sensors <NUM>. In some examples, each of the membrane sensors <NUM> is powered by independent power sources (e.g., batteries, smart cloths, etc.) and include one or more antennas to transmit data (e.g., the limb sensor output <NUM>) to the controller <NUM>. In some examples, the wires <NUM> can be omitted.

<FIG> is an example diagram <NUM> illustrating example sensor outputs <NUM>-<NUM> of the example membrane sensors <NUM> (e.g., the first membrane sensor <NUM>, the second membrane sensor <NUM>, the third membrane sensor <NUM>) of <FIG>. In operation, the first membrane sensor <NUM> provides first ones of the first sensor outputs (e.g., the limb sensor outputs <NUM>). Specifically, during movement of the shoulder <NUM>, the first membrane sensor <NUM> generates a first sensor output <NUM>. Based on a position of the elbow <NUM> (e.g., bent or straight), the second membrane sensor <NUM> generates a second sensor output <NUM>. Based on a position of the hand (e.g., bent or straight at the wrist) and/or a position of a forearm (e.g., twist position or rotational position relative to a longitudinal axis along the forearm), the third membrane sensor <NUM> of the illustrated example generates a third sensor outputs <NUM>.

Each example of the sensor outputs <NUM>-<NUM> is representative of movements of the arm 102a relative to an initial position (e.g., the arm 102a positioned against the side of the body <NUM> with palm against the body <NUM>). The example sensor outputs <NUM>-<NUM> are representative of, and/or can be used to, detect an amount of strain imparted to the arm 102a during movement as the shoulder <NUM> rotates relative to the body <NUM>, the elbow <NUM> bends at the elbow joint, the hand bends at the wrist <NUM>, the arm 102a rotates relative to the shoulder <NUM>, the forearm twists relative to the elbow and/or the shoulder, and/or any other position of the arm 102a relative to the body <NUM>. The other positions can include various positions (e.g., rotating the arm 102a outward, lifting the arm 102a above a user's head, rotating the arm 102a in a circle, etc.). The outputs <NUM>-<NUM> can be a voltage signal, a current signal and/or any other type of signal.

<FIG> illustrate an example membrane sensor <NUM> that can implement membrane sensors <NUM> of the example ergonomics improvement system <NUM> of <FIG> and <FIG>. The membrane sensor <NUM> of the illustrated example includes a membrane <NUM> (e.g., a membrane layer) and a sensor <NUM> (e.g., a sensor layer). <FIG> is a side view of the example membrane sensor <NUM>. <FIG> is a top view of the example membrane <NUM> and <FIG> is an enlarged portion of the example membrane <NUM> of <FIG> is a top view of the example sensor <NUM> and <FIG> is an enlarged portion of the example sensor <NUM> of <FIG>. For example, the membrane sensor <NUM> of the illustrated example can implement the first membrane sensor <NUM>, the second membrane sensor <NUM> and/or the third membrane sensor <NUM> of <FIG>. The membrane sensor <NUM> of <FIG> can be formed or shaped to similar to the first membrane sensor <NUM>, the second membrane sensor <NUM>, the third membrane sensor <NUM>, and/or can have any other shape (e.g., a waist band, a belt, etc.) to fit on and/or around a portion of the body <NUM>. Additionally, the membrane sensor <NUM> of the illustrated example is flexible and can conform (e.g., bend, wrap, etc.) to portions (e.g., the shoulder <NUM>, the elbow <NUM>, the wrist <NUM> of <FIG>) of the body <NUM>.

Referring to <FIG>, the membrane <NUM> of the illustrated example couples to the sensor <NUM> via adhesive <NUM> (e.g., an adhesive layer <NUM>). In the illustrated example, the adhesive <NUM> is positioned between the membrane <NUM> and the sensor <NUM>. The adhesive <NUM> can include, but is not limited to, a plastic, a tape, glue, a paste, and/or any other type of adhesive. The membrane <NUM> of the illustrated is a hexagonal meta-membrane. For example, the membrane <NUM> of <FIG> includes a first frame <NUM>. To improve or increase flexibility characteristics of the membrane sensor <NUM>, the first frame <NUM> includes a plurality of first openings or cutouts <NUM>. As a result, the frame <NUM> includes a plurality of flexible legs <NUM> (e.g., strips, frame portions, etc.) that are formed by the first cutouts <NUM>. The first frame <NUM> (e.g., via the first legs <NUM> and/or the first cutouts <NUM>) defines a first pattern <NUM> (e.g., an auxetic hexagonal pattern). In particular, the first pattern <NUM> is an Auxetic or hexagonal pattern. The membrane <NUM> can be rubber, plastic, aluminum, copper, and/or any other material(s) that can flex or conform to a part of the body <NUM>.

Referring to <FIG>, the sensor <NUM> is an electrical sensor (e.g., a strain sensor) that generates electrical outputs based on a flexed position of the sensor <NUM>. For example, the sensor <NUM> of <FIG> can be a strain sensor, piezoelectric sensor, a flex circuit and/or any other flexible sensor that provides and/or generates output signals (e.g., the limb sensor outputs <NUM>) when flexed, bent, and/or otherwise moved relative to an initial position. For example, the electrical signals output by the sensor <NUM> can be communicated to the controller <NUM> via the wires <NUM> (<FIG>).

The sensor <NUM> of the illustrated example includes a second frame <NUM>. To improve or increase flexibility and/or stretchability characteristics of the membrane sensor <NUM>, the second frame <NUM> includes a plurality of second openings or cutouts <NUM>. As a result, the second frame <NUM> includes a plurality of flexible second legs <NUM> (e.g., strips, frame portions, etc.) that are formed by the second cutouts <NUM>. The second frame <NUM> (e.g., via the second legs <NUM> and/or the second cutouts <NUM>) defines a second pattern <NUM> (e.g., auxetic hexagonal pattern). In particular, the second pattern <NUM> is an Auxetic. In the illustrated example, the first pattern <NUM> of the membrane <NUM> is complementary (e.g., identical) to the second pattern <NUM>. For example, <FIG> are enlarged views of the membrane <NUM> of <FIG> and the sensor <NUM> of <FIG>, respectively. Referring to <FIG>, each of the first pattern <NUM> and the second pattern <NUM> includes substantially similar (e.g., identical) dimensional characteristics. As used herein, "substantially similar identical dimensional characteristics" means that dimensions of the membrane <NUM> and the sensor <NUM> are identical or within a certain manufacturing tolerance (e.g., between approximately <NUM> percent and <NUM> percent). The first pattern <NUM> and the second pattern <NUM> include a plurality of interconnected triangular-shaped portions or sections <NUM> that each include a dimensional length La, a dimensional height Ha, an angle α, a thickness Wa, and radii r as shown in <FIG>. For example, the dimensional height Ha is a length of a base 328a of a triangular section 326a. The angle α symbol is an of respective side legs 328b, 328c of the triangular section 326a relative to horizontal. The dimension La is a distance between a tip 328d of the triangular section 326a and the base 328a of the triangular section 326a. The tip 328d is defined by respective ends of the legs 328b, 328c opposite the base 328a. The respective ends of the legs 328b, 328c proximate the tip 328d are not connected (e.g., are disconnected) to form a gap <NUM> therebetween. The radius r is a radius of corners of the triangular-shaped sections <NUM>. The dimension Wa is a width of the legs <NUM>, <NUM> of the respective first and second patterns <NUM>, <NUM>. The dashed lines in <FIG> are part of dimension lines and form no part of the first pattern <NUM> and the second pattern <NUM>. Additionally, the first cutouts <NUM> extend (e.g., completely) through a meta-membrane thickness <NUM> (<FIG>) of the membrane <NUM> and the second cutouts <NUM> extend (e.g., completely) through a sensor device thickness <NUM> (e.g., <FIG>) of the sensor <NUM>. However, in some examples, the first cutouts <NUM> and/or the second cutouts <NUM> can be formed as recessed cavities that do not extend (e.g., completely) through (or partially extend through a portion) the respective meta-membrane thickness <NUM> and sensor device thickness <NUM> of the membrane <NUM> and/or the sensor <NUM>. Table <NUM> below provides example dimensional values that can be used to implement the first pattern <NUM> and/or the second pattern <NUM>. The example dimensions are provided as an example and the membrane sensor <NUM> is not limited to the parameters, values, and units shown. In other examples, the membrane sensor <NUM> can be formed with any other dimensional values.

<FIG> illustrate another example membrane sensor <NUM> disclosed herein that can implement the example ergonomics improvement system <NUM> of <FIG> and <FIG>. The membrane sensor <NUM> of the illustrated example includes a membrane <NUM> (e.g., a membrane layer) and a sensor <NUM> (e.g., a sensor layer). <FIG> is a side view of the example membrane sensor <NUM>. <FIG> is a top view of the example membrane <NUM> and <FIG> is an enlarged portion of the example membrane <NUM> of <FIG> is a top view of the example sensor <NUM> and <FIG> is an enlarged portion of the example sensor <NUM> of <FIG>. For example, the membrane sensor <NUM> of the illustrated example can implement the first membrane sensor <NUM>, the second membrane sensor <NUM> and/or the third membrane sensor <NUM> of <FIG>. The membrane sensor <NUM> of <FIG> can be formed or shaped similar to the first membrane sensor <NUM>, the second membrane sensor <NUM>, the third membrane sensor <NUM>, and/or can have any other shape (e.g., a waist band, a belt, etc.) to fit on and/or around a portion of the body <NUM>. Additionally, the membrane sensor <NUM> of the illustrated example is flexible and can conform (e.g., bend, wrap, etc.) to portions (e.g., the shoulder <NUM>, the elbow <NUM>, the wrist <NUM> of <FIG>) of the body <NUM>.

Referring to <FIG>, the membrane <NUM> of the illustrated example couples to the sensor <NUM> via adhesive <NUM> (e.g., an adhesive layer). The adhesive <NUM> can include a plastic, tape, glue, latex strip, and/or any other type of adhesive. In the illustrated example, the adhesive <NUM> is positioned between the membrane <NUM> and the sensor <NUM>. The membrane <NUM> of the illustrated example includes a first frame <NUM>. The first frame <NUM> of <FIG> includes a plurality of first openings or cutouts <NUM> defining a first pattern <NUM> to improve or increase flexibility and/or stretchability characteristics of the membrane sensor <NUM>. Specifically, the first pattern <NUM> of the illustrated example is a Kirigami pattern (e.g., a bi-axial Kirigami pattern). The membrane <NUM> can be rubber, plastic, aluminum, copper, and/or any other material(s) that can flex or conform to a part of the body <NUM>.

The sensor <NUM> includes a second frame <NUM> having a plurality of second openings or cutouts <NUM> defining a second pattern <NUM>. In particular, the second pattern <NUM> of the illustrated example is a Kirigami pattern. In other words, the first pattern <NUM> is complementary (e.g., identical) to the second pattern <NUM>. For example, <FIG> are enlarged views of the membrane <NUM> of <FIG> and the sensor <NUM> of <FIG>, respectively.

Referring to <FIG>, each of the first pattern <NUM> and the second pattern <NUM> includes substantially similar (e.g., identical) dimensional characteristics. As used herein, "substantially similar identical dimensional characteristics" means that dimensions of the membrane <NUM> and the sensor <NUM> are identical or within a certain manufacturing tolerance (e.g., between approximately <NUM> percent and <NUM> percent). For example, the first cutouts <NUM> of the first pattern <NUM> have a first set 410a of the first cutouts <NUM> positioned in a first orientation and a second set 410b of the first cutouts <NUM> positioned in a second orientation different than the first orientation. For example, the first set 410a of the first cutouts <NUM> is substantially perpendicular to the second set 410b of the first cutouts <NUM>. Likewise, for example, the second cutouts <NUM> of the second pattern <NUM> have a first set 420a of the second cutouts <NUM> positioned in a first orientation and a second set 420b of the second cutouts <NUM> positioned in a second orientation different than the first orientation. For example, the first set 420a of the second cutouts <NUM> is substantially perpendicular (e.g., perfectly orthogonal or almost perfectly orthogonal (e.g., within <NUM> degrees of perpendicularity)) to the second set 420b of the second cutouts <NUM>. The first cutouts <NUM> and the second cutouts <NUM> each include a length Hb and a width Wb. Additionally, a distance Wc separates respective ones of the first set 410a of the first cutouts 410a and respective ones of the second set 410b of the first cutouts <NUM>. Likewise, a distance Wc separates respective ones of the first set 420a of the second cutouts <NUM>, and respective ones of the second set 420b of the second cutouts <NUM>. A length La is a distance between respective ones of the first set 410a of the first cutouts <NUM>, and respective ones of the first set 420a of the second cutouts <NUM>. The length Lb is a distance between respective ones of the second set 410b of the first cutouts <NUM> and respective ones of the second set 420b of the second cutouts <NUM>. In the illustrated example, the width Wb and the distance Wc are equivalent. Likewise, the length La and length Lb are equivalent. However, in some examples, the width Wb and the distance Wc and/or the length La and length Lb can be different values. Additionally, the first set 410a of the first cutouts <NUM> can be oriented at an angle relative to the second set 410b of the first cutouts <NUM> and/or the first set 420a of the second cutouts <NUM> can be oriented at an angle relative to the second set 420b of the second cutouts <NUM>. In some examples the first cutouts <NUM> and/or the second cutouts <NUM> can have any other suitable pattern. Additionally, the first cutouts <NUM> extend (e.g., completely) through a meta-membrane thickness <NUM> (<FIG>) of the membrane <NUM> and the second cutouts <NUM> extend (e.g., completely) through a sensor device thickness <NUM> (<FIG>) of the sensor <NUM>. However, in some examples, the first cutouts <NUM> and/or the second cutouts <NUM> can be formed as recessed cavities (e.g., slots, slits, channels, etc.) that do not extend (e.g., completely) through (or partially extend through a portion) the respective meta membrane thickness <NUM> and sensor device thickness <NUM> of the membrane <NUM> and/or the sensor <NUM>. Table <NUM> below provides example dimensional values that can be used to implement the first pattern <NUM> and/or the second pattern <NUM>. The example dimensions are provided as an example and the membrane sensor <NUM> is not limited to the parameters, values, and units shown. In other examples, the membrane sensor <NUM> can be formed with any other dimensional values.

<FIG> is a schematic illustration of example displacement and stress distribution of a membrane sensor <NUM>, the example membrane sensor of <FIG> and the example membrane sensor of <FIG>. The membrane sensor <NUM> includes a membrane <NUM> that is formed without cutouts or openings. <FIG> illustrates side views of the membranes sensor <NUM>, the membrane sensor <NUM> and the membrane sensor <NUM> labeled as (a), (c), and (e), respectively. <FIG> also illustrates top views of the membranes sensors <NUM>, <NUM>, <NUM> labeled as (b), (d), and (f), respectively. The membrane sensor <NUM>, the membrane sensor <NUM> and the membrane sensor <NUM> are shown in respective flexed or stretched positions when a similar or identical force (or a flex position of the arm 102a) is imparted to the respective membrane sensors <NUM>, <NUM>, <NUM>. <FIG> illustrates differences in flexibility between membrane sensors <NUM>, <NUM>, and <NUM>. The membrane sensor <NUM> can flex greater than the membrane sensor <NUM> by a height <NUM> (e.g., approximately between <NUM>% and <NUM>% greater flexibility). The membrane sensor <NUM> can flex greater than the membrane sensor <NUM> by a height <NUM> (e.g., between approximately <NUM>% and <NUM>% greater flexibility than the membrane sensor <NUM> and/or between approximately <NUM>% and <NUM> greater flexibility than the membrane sensor <NUM>). Stress-strain mapping is shown in the membrane sensors <NUM>, <NUM> and <NUM> when the membrane sensors <NUM>, <NUM>, <NUM> are flexed to the positions shown in <FIG>. A strain key <NUM> to indicate levels of strain. Although the membrane sensor <NUM> stretches or flexes the least amount, the membrane sensor <NUM> experiences a greater amount of strain and/or stress compared to the membrane sensors <NUM> and <NUM>.

<FIG> illustrate other example membrane sensors 600a-d disclosed herein that can be used to implement the ergonomics improvement system of <FIG> and <FIG>. The membrane sensors 600a-d (e.g., strain sensors, flex circuit) can be assembled in various configurations including, for example, including a first membrane sensor 600a, a second membrane sensor 600b, a third membrane sensor 600c, and a fourth membrane sensor 600d. For example, the membrane sensors 600a-600d can implement the example membrane sensors <NUM> of <FIG>, the membrane sensors <NUM> of <FIG>, and/or the membrane sensors <NUM> of <FIG>.

The first membrane sensor 600a includes a membrane <NUM> (e.g., a wearable membrane), a sensor <NUM> (e.g., strain sensing element), and a first adhesive <NUM> (e.g., an adhesive layer) that can couple or attach (e.g., directly) to skin <NUM> of the user 106a. In the illustrated example, the membrane <NUM> attaches to the skin <NUM> of the user 106a. The first adhesive <NUM> is positioned between the membrane <NUM> and the sensor <NUM> and couples or attaches the membrane <NUM> and the sensor <NUM>. When coupled to the body <NUM>, the membrane <NUM> is between a first side of the first adhesive <NUM> and the skin <NUM> of the user 106a (e.g., above the skin <NUM>), and the sensor <NUM> is positioned adjacent or proximate (e.g., directly engaged with) a second side of the first adhesive <NUM> opposite the first side.

The second membrane sensor 600b includes the sensor <NUM>, the first adhesive <NUM>, the membrane <NUM>, and a second adhesive <NUM>. The second adhesive <NUM> can be used to couple (e.g., directly couple) the membrane <NUM> to the skin <NUM> of the user 106a. The membrane <NUM> is positioned between the first adhesive <NUM> and the second adhesive <NUM>, and the second adhesive <NUM> is positioned between the membrane <NUM> and the skin <NUM> when coupled to the body <NUM>. The first adhesive is positioned between and couples the membrane <NUM> and the sensor <NUM>.

The third membrane sensor 600c includes the membrane <NUM> positioned between the sensor <NUM> and the first adhesive <NUM>. For example, the sensor <NUM> attaches to and/or is integrally formed with the membrane <NUM>. The first adhesive <NUM> couples or attaches the membrane <NUM> and the sensor <NUM> to clothing <NUM> to be worn by the user 106a. When worn by the user 106a, the clothing <NUM> retains or maintains the membrane sensor 600a on the user 106a. The sensor <NUM> is positioned proximate (e.g., directly engaged with) the skin <NUM> of the user 106a when the clothing <NUM> having the membrane sensor 600c is worn by the user 106a. In other words, the sensor <NUM> is inside or located on an interior side of the clothing <NUM> when the clothing <NUM> is worn by the user 106a.

The fourth membrane sensor 600d includes the sensor <NUM>, the first adhesive <NUM>, the membrane <NUM>, the second adhesive <NUM> and the clothing <NUM>. The first adhesive <NUM> couples or attaches the membrane <NUM> and the sensor <NUM>. In other words, the first adhesive is positioned between the sensor <NUM> and the membrane <NUM>. The second adhesive <NUM> attaches the membrane <NUM> and the clothing <NUM>. In other words, the second adhesive is positioned between the membrane <NUM> and the clothing <NUM>. When worn by the user 106a, the clothing <NUM> is positioned proximate the skin <NUM> of the user 106a. In other words, the sensor <NUM> is exposed or located on an exterior side of the clothing <NUM> when the clothing <NUM> is worn by the user 106a.

The sensors <NUM> (e.g., and the sensor <NUM> of <FIG> and/or the sensor <NUM> of <FIG>) can be various types of sensors (e.g., strain sensors). For example, the sensors <NUM> of <FIG> (e.g., and the sensors <NUM> of <FIG> and/or the sensors <NUM> of <FIG>) can include, but are not limited to, a load cell sensor, piezoelectric devices or sensors, flexible circuit boards, conductive materials including carbon nanomaterials (e.g., carbon blacks [CBs], carbon nanotubes [CNTs], graphene and its derivatives), metal nanowires (NWs), nanofibers (NFs), and nanoparticles (NPs), MXenes (e.g., Ti3C2Tx), ionic liquid, hybrid micro-/nanostructures, conductive polymers, and/or any other strain and/or stress sensing material(s) or sensor(s) that can generate output signals (e.g., electrical signals, the limb sensor outputs <NUM>) when flexed, bent, and/or otherwise distorted.

The membrane <NUM> (e.g., and the membrane <NUM> of <FIG> and/or the membrane <NUM> of <FIG>) can be formed of various types of materials including, but not limited to, silicone elastomers (e.g., ecfoex and polydimethylsilowane [PDMS]), rubbers, thermoplastic polymers, medical adhesive films, thermoplastic polyurethane (TPU), polystyrene-based elastomers, PDMS, natural fiber-based materials such as cotton, wool, flax, and/or any other material(s) having flexible characteristics.

The membrane sensor <NUM>, <NUM>, <NUM>, and 600a-600d can have various thicknesses in a z-direction (e.g., stack-up direction/cross-section). In some examples, a thickness of the membrane <NUM>, <NUM> and/or <NUM> can be the same or different than as a thickness of the sensor <NUM>, <NUM> and/or <NUM>. The membrane sensor <NUM>, <NUM>, <NUM> and/or 600a-d, the membrane <NUM>, <NUM>, <NUM>, and/or the sensor <NUM>, <NUM>, <NUM> can be formed via molding (e.g., injection molding), additive manufacturing (e.g., 3D-printing), lithography, a combination thereof, and/or any other manufacturing process(es).

<FIG> is an example lower body sensor system <NUM> disclosed herein that can be used to implement the example ergonomics improvement system <NUM> of <FIG>. The lower body sensor system <NUM> of the illustrated example implements the load sensor <NUM> and the position sensor <NUM> of <FIG>. The load sensor <NUM> includes load cells <NUM> and the position sensor <NUM> includes Light Detection and Ranging (LiDAR) sensors <NUM> (e.g., a pressure pad, step scan sensor, etc.). The load cells <NUM> and the LiDAR sensors <NUM> are incorporated (e.g., carried by, attached, or otherwise embedded) in a pair of shoes <NUM> to be worn by the user 106a. To detect position of the user's feet, the LiDAR sensors <NUM> emits pulsed waves into a surrounding environment. When the user stands with his feet together, the pulses bounce off the opposing shoe and return to the sensor. The sensor uses a time differential for each pulse to return to the sensor to calculate a distance traveled. When a first foot is forward and/or rearward of the other foot, the pulsed waves project into the surrounding environment instead of an otherwise opposing shoe, indicating that the user's feet are spread apart. Thus, pulses emitted by the LiDAR sensors <NUM> can be used to determine if the user 106a is standing in a stable or bracing position (e.g., with one foot spaced apart and in front of their other foot) or a non-stable or non-bracing position (e.g., a user standing with their feet spaced apart, but the left foot substantially in line with the right foot) when performing the physical tasks.

<FIG> is another example lower body sensor system <NUM> of the example ergonomics improvement system <NUM> of <FIG>. The lower body sensor system <NUM> of the illustrated example implements the load sensor <NUM> and the position sensor <NUM> of <FIG>. The load sensor <NUM> includes load cells <NUM> and the position sensor <NUM> includes pressure sensors <NUM> (e.g., a pressure pad, step scan sensor, etc.). The load cells <NUM> and the pressure sensors <NUM> are located in (e.g., embedded in the soles of) a pair of shoes <NUM> that can be worn by the user 106a (<FIG>). The load cells <NUM> measure a load or weight of the user 106a to determine an amount of weight that the user 106a is holding or lifting. The pressure sensors <NUM> of the illustrated example can detect and/or determine a stance (e.g., feet positioning) of the user 106a performing a physical task. For example, the pressure sensors <NUM> can detect and/or otherwise determine if a user is standing in a stable or bracing position (e.g., with one weight evenly distributed in the feet) or a non-stable or non-bracing position (e.g., a user standing with their weight all centered forward on the toes or all centered backwards on the heels) when performing a physical task. In some examples, the pressure sensors <NUM> can be used to determine weight distribution of the user (e.g., whether the weight distribution is centered). For example, a weight of the user 106a being offset toward the heels of the user 106a can indicate that the user 106a is off-balance and/or at risk of falling or being injured. In some examples, by determining a position of the arm 102a via the upper body sensor systems 111a, the position of each foot of the user 106a via the position sensor <NUM> and a load carried by the user 106a via the load sensor <NUM>, the ergonomics improvement system <NUM> can determine if the user's stance is stable (e.g., or optimal) for carrying a detected load (e.g., the object <NUM> of <FIG>).

<FIG> are schematic illustrations of example third outputs <NUM> of the example lower body sensor system <NUM> of <FIG>. <FIG> illustrates a first one <NUM> of the third outputs <NUM> and <FIG> illustrates a second one <NUM> of the third outputs <NUM>. For example, the first one <NUM> of the third outputs <NUM> is representative of the user 106a having his/her feet spaced apart, but a left foot <NUM> substantially even with a right foot <NUM>. The second one <NUM> of the third outputs <NUM> of <FIG> is representative of the user 106a having the right foot <NUM> spaced apart and in front of the left foot <NUM>. The pressure sensors <NUM> generate Stepscan or pressure outputs, such as shown in <FIG>. The third outputs <NUM> of the pressure sensors <NUM> can detect pressure distribution across the feet of the user 106a. For example, a white colored area <NUM> in <FIG> indicates an area with low pressure, a grey colored area <NUM> in <FIG> indicates medium pressure, and a black colored area <NUM> in <FIG> indicates high pressure. In <FIG> the user has more pressure on his/her right foot <NUM> as indicated by more grey colored area <NUM> and more black colored area <NUM> as compared to the left foot <NUM>, which has more white colored area <NUM>. <FIG> illustrates the weight of the user 106a concentrated in the back heel of the right foot <NUM> and concentrated on a pad or middle area of the left foot <NUM>.

<FIG> is a block diagram of the example controller <NUM> of the example ergonomics improvement system <NUM> of <FIG>. The controller <NUM> includes a sensor manager <NUM>, a data monitor <NUM>, a warning device manager <NUM>, and a calibrator <NUM>. The sensor manager <NUM>, the data monitor <NUM>, the warning device manager <NUM> and the calibrator <NUM> are communicatively coupled via a bus <NUM>.

The sensor manager <NUM> receives inputs from the limb sensor <NUM>, the load sensor <NUM>, or/and the position sensor <NUM>. For example, the sensor manager <NUM> receives the limb sensor outputs <NUM>, the load sensor outputs <NUM>, and/or the position sensor outputs <NUM>. For example, the sensor manager <NUM> receives the outputs <NUM>-<NUM>, the outputs from the load cells <NUM>, and the outputs from the pressure sensors <NUM> and/or the LiDAR sensors <NUM>. The sensor manager <NUM> receives the outputs as currents, voltages, etc. In some examples, the sensor manager <NUM> can condition the signals for processing by the data monitor <NUM>. In some examples, the sensor manager <NUM> converts the inputs to binary values (e.g., on/off), digital values, and/or an analog values. For example, the sensor manager <NUM> can convert the signals of the position sensor <NUM> to binary values.

For example, the sensor manager <NUM> can provide binary values "<NUM>" for respective ones of the outputs <NUM>-<NUM> of the in response to the output signals not exceeding a threshold value (e.g., an electric current) associated with the respective ones of the membrane sensors <NUM>, <NUM>, <NUM> and can provide binary values "<NUM>" for respective ones of the outputs <NUM>-<NUM> of the membrane sensors <NUM>, <NUM>, <NUM> in response to the output signals exceeding a threshold value (e.g. an electric current) associated with the respective ones of the membrane sensors <NUM>, <NUM>, <NUM>. For example, the sensor manager <NUM> can provide a binary value "<NUM>" when the position sensor <NUM> provides signals representative of the user 106a being in the stable stance and a binary value "<NUM>" when the position sensor <NUM> provides signals representative of the user 106a being in a non-stable stance. In some examples, the sensor manager <NUM> can provide a binary value "<NUM>" in response to the load sensor <NUM> providing a signal representative of a weight that is greater than a threshold (e.g., <NUM> pounds, i.e. approximatively <NUM>) and a binary value "<NUM>" in response to the load sensor <NUM> providing a signal representative of a weight being less than the threshold.

The data monitor <NUM> stores and processes the signal(s) from the sensor manager <NUM>. The data monitor <NUM> can compare signal(s) from the sensor manager <NUM> to a threshold. In some examples, the threshold can be obtained, retrieved or otherwise accessed from memory by the data monitor <NUM>. For example, the data monitor <NUM>, via a comparator, can compare the signals from the sensor manager <NUM> to a table to determine if the user 106a is performing a non-ergonomic or improper activity based on the data provided by the limb sensor outputs <NUM>, the load sensor outputs <NUM>, and/or the position sensor outputs <NUM>. For example, data monitor <NUM> can compare the signals from the sensor manager <NUM> to threshold values stored in a look-up table associated with respective thresholds for the respective ones of the limb sensor outputs <NUM>, the load sensor outputs <NUM> and/or the position sensor output <NUM>. For example, the data monitor <NUM> can compare a determined position of the limb <NUM> to a position threshold associated with a measured load carried by the user 106a provided by the load sensor <NUM> and a determined position of the right foot <NUM> relative to the left foot <NUM>. The data monitor <NUM> can communicate a warning activation signal to the warning device manager <NUM> in response to determining that the detected position of the limb <NUM> exceeds a position threshold (e.g., from a look-up table) associated with or corresponding to the measured load from the load sensor <NUM> and/or the detected position of the right foot <NUM> relative to the left foot <NUM>. For example, the outputs <NUM>-<NUM> of <FIG> can be indicative of non-ergonomic or improper movement or position of the limb <NUM> if a load carried by the user 106a exceeds a threshold load and/or a stance of the user 106a is a non-stable stance (e.g., a stance shown in <FIG>). In some instances, the outputs <NUM>-<NUM> of <FIG> can be indicative of ergonomic or proper movement or position of a limb <NUM> if a load carried by the user 106a does not exceed a threshold load and/or a stance of the user 106a is a stable stance (e.g., a stance shown in <FIG>).

For example, the look-up table can have a plurality of first threshold values corresponding to outputs from the membrane sensors <NUM>, <NUM>, <NUM>. Based on a comparison of the outputs from the membrane sensors <NUM>, <NUM>, <NUM> and the thresholds corresponding to the respective ones of the membrane sensors <NUM>, <NUM>, <NUM> stored in the lookup table, the weight provided by the load sensor <NUM>, and the feet stance provided by the position sensor <NUM>, the data monitor <NUM> determines if the user 106a is conducting activity (e.g., based on limb movement or position) that is ergonomically proper or ergonomically improper. If one or more signals or a combination of signals from the sensor manager <NUM> exceeds one or more thresholds or a combination of thresholds compared to the limb sensor outputs <NUM>, the load sensor outputs <NUM> and the position sensor outputs <NUM>, then the warning device manager <NUM> triggers the warning signal <NUM> to trigger an alarm (e.g., indicative of a non-ergonomic activity or movement).

The warning device manager <NUM> can receive a signal from the data monitor <NUM> if the signal from the sensor manager <NUM> exceeds a threshold. The warning device manager <NUM> can send the warning signal <NUM> and/or alarm. Example alarms disclosed herein include, but are not limited to, visual alarms (e.g., a light), audio alarms (e.g., a speaker), haptic feedback, a combination thereof and/or any other alarm(s).

The calibrator <NUM> instructs users of motions to complete calibration such as those illustrated in <FIG>. The calibrator <NUM> also stores movement data from various positions from the calibration and can process the movement data to be used as thresholds for the data monitor <NUM>. The calibrator <NUM> sets a zero or reference value for the limb sensor <NUM>, the load sensor <NUM> and the position sensor <NUM>.

Alternatively, the controller <NUM> of the illustrated example can be configured to communicate the sensor outputs (e.g., the sensor outputs <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, <NUM> etc.) from the upper body sensor system 111a and/or the lower body sensor system 111b to a remote electronic device such as, for example, a server, a computer, a control room, a mobile device, a mobile phone, and/or any other computing device communicatively coupled to the controller <NUM> of the ergonomics improvement system <NUM>. For example, the controller <NUM> and/or the sensor manager <NUM> can transmit or communicate one or more outputs provided by the sensors (e.g., the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, the membrane sensors <NUM>, <NUM>, <NUM>, the load cells <NUM>, the pressure sensors <NUM>, the LiDAR sensors <NUM> and/or any other sensor(s)). The remote electronic device can be configured to model the movement of the user 106a (e.g., the arm 102a of the user 106a) based on the data provided by the controller <NUM>. The remote electronic device can be configured to detect whether the model represents movements that can be indicative of movements that can be ergonomic or acceptable, or movements that can be non-ergonomic or not acceptable. If the remote electronic device determines that the movements of the user 106a are acceptable, the remote electronic device does not communicate with the controller <NUM>. If the remote electronic device determines that the movements of the user 106a are not acceptable, the remote electronic device communicate instructions to the controller <NUM> to cause the warning device manager <NUM> to initiate the warning signal <NUM> to active the warning device <NUM>.

While an example manner of implementing the controller <NUM> of <FIG> is illustrated in <FIG>, one or more of the elements, processes and/or devices illustrated in <FIG> may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the sensor manager <NUM>, the data monitor <NUM>, the warning device manager <NUM>, and the calibrator <NUM>. and/or, more generally, the example controller of <FIG> may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the sensor manager <NUM>, the data monitor <NUM>, the warning device manager <NUM>, and the calibrator <NUM>. and/or, more generally, the example controller <NUM> of <FIG> could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the sensor manager <NUM>, the data monitor <NUM>, the warning device manager <NUM>, and the calibrator <NUM> and/or, more generally, the example controller <NUM> of <FIG> is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware. Further still, the example controller <NUM> of <FIG> may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in <FIG>, and/or may include more than one of any or all of the illustrated elements, processes and devices. As used herein, the phrase "in communication," including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events.

A flowchart representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the ergonomics improvement system <NUM> of <FIG> is shown in <FIG> and <FIG>. The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by a computer processor such as the processor <NUM> shown in the example processor platform <NUM> discussed below in connection with <FIG>. The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associated with the processor <NUM>, but the entire program and/or parts thereof could alternatively be executed by a device other than the processor <NUM> and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowcharts illustrated in <FIG> and <FIG>, many other methods of implementing the example ergonomics improvement system <NUM> may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally, or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware.

The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data (e.g., portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc. in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and stored on separate computing devices, wherein the parts when decrypted, decompressed, and combined form a set of executable instructions that implement a program such as that described herein.

In another example, the machine readable instructions may be stored in a state in which they may be read by a computer, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc. in order to execute the instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, the disclosed machine readable instructions and/or corresponding program(s) are intended to encompass such machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit.

As mentioned above, the example processes of <FIG> and <FIG> may be implemented using executable instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media.

The method <NUM> of <FIG> is an example method for implementing the ergonomics improvement system <NUM> of <FIG>. The method <NUM> begins at block <NUM>, with the sensor manager <NUM> receiving data collected from sensor(s). The sensor(s) can include the limb sensor <NUM>, the load sensor <NUM>, the position sensor <NUM>, the membrane sensors <NUM>, <NUM>, <NUM>, the load cells <NUM>, the pressure sensor <NUM>, the LiDAR sensors <NUM> and/or any other sensor(s).

At block <NUM>, the data monitor <NUM> compares data (e.g., signals(s)) from the sensor manager <NUM> data to a threshold. The threshold can be obtained from a lookup table that can be stored in a database or memory of the controller <NUM>.

At block <NUM>, the data monitor <NUM> determines whether the threshold at block <NUM> is exceeded. If the data monitor <NUM> determines that the threshold is exceeded at block <NUM>, then the process continues to block <NUM>. At block <NUM>, the warning device manager <NUM> initiates a warning signal (e.g., the warning signal <NUM>) to activate the alarm and/or warning device <NUM>. If the data monitor <NUM> determines at block <NUM> that the threshold is not exceeded, then the process returns to block <NUM>.

Referring to <FIG>, the method <NUM> is an example method to calibrate the upper body sensor system 111a and the lower body sensor system 111b of the example ergonomics improvement system <NUM> of <FIG>. For example, calibration can be implemented using the calibrator <NUM>. For example, calibration of the example ergonomics improvement system <NUM> of <FIG> can occur when the system is initially turned on and/or at any other time when the system is in use. In some examples, calibration can be automatically set to occur at pre-defined intervals or at certain events such as when the controller <NUM> detects outlier values outputted by one or more sensors of the ergonomics improvement system of <FIG>.

At block <NUM>, the example ergonomics improvement system <NUM> of <FIG> can detect the upper body sensor system 111a (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) and the lower body sensor system 111b (e.g., the load cells <NUM>, the pressure sensor <NUM>, the LiDAR sensor <NUM>, etc.) via the sensor manager <NUM>. At block <NUM>, the example calibrator <NUM> instructs the user 106a to initiate sensor calibrations. Example sensor calibration positions are disclosed herein and are illustrated and discussed in <FIG>.

At block <NUM>, the example calibrator <NUM> records sensor output(s) associated with the different sensor calibrations. For example, the calibrated values for each of the sensors (e.g., the limb sensor <NUM>, the load sensor <NUM>, and/or the position sensor <NUM>) are zero values or reference values.

<FIG> is an example diagram representative of example calibration positions <NUM> disclosed herein that can be used to implement the example method <NUM> of <FIG>. The sensor calibration positions can be instructed to the user 106a using a user interface that can include, for example, a display, a speaker, a combination thereof, and/or any other communication device carried by the controller <NUM>. The example calibration positions <NUM> can be used to calibrate one or more of the membrane sensors <NUM>, <NUM>, <NUM> after the sensors are carried or coupled to the user 106a. For example, each of the membrane sensors <NUM>, <NUM>, <NUM> can be calibrated using the example calibration positions <NUM> of <FIG>. For example, the calibration positions <NUM> include three sets of calibration positions (i.e., position <NUM>, position <NUM>, position <NUM>) for each of the shoulder <NUM>, the elbow <NUM> and the hand/wrist <NUM>. However, the calibration positions are not limited to the positions shown in <FIG> and can include one or more other positions that are not shown in <FIG>.

In position <NUM> of a shoulder calibration <NUM>, the user 106a is instructed to move their arms (i.e., the arm 102a) in a forward position (e.g., a fully forward extended position in a direction in front of the user 106a) and rearward position (e.g., a fully rearward extended position in a direction behind the user 106a). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the arm 102a moves to the forward position and the rearward position.

In position <NUM> of a shoulder calibration <NUM>, the user 106a is instructed to move their arms in an upward position (e.g., a fully raised position above the user's head) and downward position (e.g., a fully extended position on the side of the user's body). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the arm 102a moves to the upward position and the downward position.

In position <NUM> of a shoulder calibration <NUM>, the user 106a is instructed to extend their arms outwardly and sideways (e.g., a wingspan formation) and rotate/twist their arms in a circular motion between a first rotational position (e.g., twist or rotate in a first rotational position) and a second rotational position (e.g., twist or rotate in a second rotational direction opposite the first direction). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the arm 102a moves to the first rotational position and the first rotational position.

In position <NUM> of an elbow calibration <NUM>, the user 106a is instructed to move their arms sideways and to move their arms to a curled position (e.g., fully curled position where the hand is proximate the shoulder <NUM>) and an extended position (e.g., a fully extended position). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) associated with the elbow <NUM> when the arm 102a moves to the curled position and the extended position.

In position <NUM> of an elbow calibration <NUM>, the user 106a is instructed to bend their elbows and move their elbows while in the bent position to a bent upward position and a bent downward position. The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the arm 102a moves to the bent upward position and the bent downward position.

In position <NUM> of the elbow calibration <NUM>, the user 106a is instructed to rotate their arms with the elbow bent between a first rotational position and a second rotational position opposite the first rotational position. The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the arm 102a, with the bent elbow <NUM>, moves to the first rotational position and the second rotational position.

In position <NUM> of a wrist/hand calibration <NUM>, the user 106a is instructed to move or bend their hand about the wrist to an upward position (e.g., fully upward position) and a downward position (e.g., a fully downward position). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the hand moves to the first rotational position and the second rotational position.

In position <NUM> of a wrist/hand calibration <NUM>, the user 106a is instructed to move their hand sideways about the wrist to a first side position (e.g., fully right side position) and a second side position (e.g., a fully left side position). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the hand moves to the first side position and the second side position.

In position <NUM> of a wrist/hand calibration <NUM>, the user 106a is instructed to twist their hand sideways about the wrist to a first rotational position (e.g., a fully rotational position in a first rotational direction) and a second rotational position (e.g., a fully rotational position in a second rotational direction). The controller <NUM> records outputs of the sensors (e.g., the membrane sensors <NUM>, <NUM>, <NUM>) when the hand moves to the first rotational position and the second rotational position.

<FIG> is a block diagram of an example processing platform structured to execute instructions of <FIG> and <FIG> to implement an example controller of example ergonomics improvement systems disclosed herein.

<FIG> is a block diagram of an example processor platform <NUM> structured to execute the instructions of <FIG> and <FIG> to implement the ergonomics improvement system <NUM> of <FIG>. The processor platform <NUM> can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™, a headset or other wearable device, or any other type of computing device.

For example, the processor <NUM> can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor implements the sensor manager <NUM>, the data monitor <NUM>, the warning device manager <NUM> and the calibrator <NUM>.

Access to the volatile memory <NUM> and the non-volatile memory <NUM> is controlled by a memory controller.

The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, and/or a voice recognition system.

The output devices <NUM> can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube display (CRT), an in-place switching (IPS) display, a touchscreen, etc.), and/or speaker.

The machine executable instructions <NUM> of <FIG> and <FIG> may be stored in the mass storage device <NUM>, in the volatile memory <NUM>, in the non-volatile memory <NUM>, and/or on a removable non-transitory computer readable storage medium such as a CD or DVD.

Claim 1:
A wearable ergonomics improvement system (<NUM>), wherein the system comprises:
a first membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) configured to couple to a shoulder (<NUM>) of a body (<NUM>, 106a), the first membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) to generate first outputs (<NUM>) in response to movement of the shoulder (<NUM>) to detect at least one of a position or rotation of the shoulder (<NUM>);
a second membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) configured to couple to an elbow (<NUM>) of the body (<NUM>, 106a), the second membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) to generate second outputs (<NUM>) in response to movement of the elbow (<NUM>) to detect at least one of a position or rotation of the elbow (<NUM>);
a third membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) configured to couple to a wrist (<NUM>) of the body (<NUM>, 106a), the third membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d) to generate third outputs (<NUM>) in response to movement of a hand (<NUM>) to detect at least one of a position or rotation of the hand (<NUM>),
a load sensor (<NUM>) configured to measure a load of the body (<NUM>, 106a),
a position sensor (<NUM>) configured to detect a position of a right foot (<NUM>) of a body relative to a left foot (<NUM>) of the body (<NUM>, 106a); and
a processor configured to:
determine a position of a limb (<NUM>) relative to the body (<NUM>, 106a) based on first outputs (<NUM>) of the first membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d), second outputs (<NUM>) of the second membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d), and third outputs (<NUM>) of the third membrane sensor (<NUM>, <NUM>, <NUM>, 600a-d);
determine a measured load based on a fourth output (<NUM>) from the load sensor (<NUM>);
determine a position of a right foot (<NUM>) of the body relative to a left foot (<NUM>) of the body based on a fifth output (<NUM>) of the position sensor (<NUM>);
compare the determined position of the limb (<NUM>) to a position threshold associated with measured load and the detected position of the right foot (<NUM>) relative to the left foot (<NUM>); and
generate a warning signal (<NUM>) in response to determining that the detected position exceeds the position threshold associated with the measured load and the detected position of the right foot (<NUM>) relative to the left foot (<NUM>).