1504 results about "Exoskeleton" patented technology

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a lower limb exoskeleton rehabilitation robot joint torque prediction method and device based on an electromyographic signal, and the method comprises the steps: firstly collecting the electromyographic, joint angle, angular velocity and joint torque data of a user, and carrying out the modeling of four muscles related to a knee joint through a neuromusculoskeletal model; and identifying physiological parameters used in the model by using a sparrow optimization algorithm, and outputting an optimal solution to obtain an estimated torque. Then, the real torque and the estimated torque are endowed with the weights of # imgabs0 # respectively, and a fusion torque is obtained; and training a neural network model by using the collected data and the fusion moment, and meanwhile, enabling the neuromusculoskeletal model to participate in the training process of the neural network model by adopting a method of bringing the neuromusculoskeletal model into a loss function, so that the constraint of the model is increased. And finally, storing the trained model for subsequent calling. The method has the advantages of high precision and strong generalization, and model training can converge more quickly under the condition of less data volume.

The embodiment of the invention provides a joint torque estimation method and device based on electromyographic signals of lower limb exoskeletons, computer equipment, a storage medium and a computer program product, and relates to the technical field of exoskeletons. The method comprises the following steps: acquiring an initial filtering electromyographic signal of a target object; obtaining an electromyographic signal envelope corresponding to the initial filtering electromyographic signal according to the initial filtering electromyographic signal; performing normalization processing on the electromyographic signal envelope to obtain an electromyographic signal value corresponding to the electromyographic signal envelope; according to the electromyographic signal value, obtaining an independent source signal corresponding to the initial filtering electromyographic signal; and obtaining a joint torque value of a target exoskeleton matched with the target object according to the independent source signal. According to the method, the accuracy and efficiency of joint torque estimation based on the electromyographic signals are improved.

The walking-assisting knee joint exoskeleton structure comprises an exoskeleton, the exoskeleton comprises an assisting assembly, a thigh supporting assembly, a shank supporting assembly and a crossed four-bar linkage assembly, the assisting assembly comprises a waistband and a driving mechanism arranged on the waistband, the thigh supporting assembly comprises a thigh binding mechanism and a thigh support, and the shank supporting assembly comprises a shank binding mechanism and a shank support. The shank supporting assembly comprises a shank binding mechanism and a shank support, and the thigh support is in power connection with the shank support through a crossed four-connecting-rod assembly. The power assisting assembly further comprises a motor assembly, a first brake cable and a second brake cable, and the first brake cable and the second brake cable are connected with the front end and the rear end of the shank binding mechanism respectively. The motor assembly is moved upwards to the waist, the load of the knee joint area is reduced, joint abrasion and muscle fatigue can be reduced, the wearing comfort and the feasibility of long-time walking are improved, the trousers bear part of the weight of the exoskeleton, the load of the legs is reduced, and the overall attractiveness is further improved.

The invention discloses a man-machine exoskeleton cooperation method based on multi-modal signal fusion, and relates to the technical field of exoskeleton program control. The method comprises the following steps that man-machine exoskeleton driving pre-pressure cold and hot mutation analysis is conducted in combination with man-machine exoskeleton man-machine cooperation components; and performing memory alloy spring pre-pressure optimization adjustment in combination with a man-machine exoskeleton driving pre-pressure cold and hot mutation analysis result. According to the invention, cold chain exoskeleton cooperative control is realized through multi-mode signal fusion, and three-level regulation and control are implemented based on a current threshold value and a myoelectricity state: graded current rising / falling and safety early warning are carried out in case of abnormal current; dynamic gain compensation or joint retardation protection is carried out when myoelectricity is abnormal; according to the man-machine exoskeleton cooperative control method and the man-machine exoskeleton cooperative control system, the effect of improving the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is achieved, and the problem that the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is disabled in the prior art is solved.

The invention discloses a lower limb exoskeleton gait track prediction method based on an LSTM-KAN fusion model, and the method comprises the steps: collecting human motion data through a sensor assembly, and carrying out the filtering, missing value processing and normalization of the human motion data; then constructing an overall architecture of a prediction model based on an LSTM-KAN network, optimizing parameters of the prediction model by using a particle swarm optimization (PSO) algorithm, extracting key features in the preprocessed data as a training data set, and inputting the training data set into the prediction model for training; and finally, collecting current human body motion data, pre-processing the current human body motion data, inputting the pre-processed current human body motion data into the trained prediction model, and outputting future human body gaits and tracks by the prediction model. And the controller takes a future gait track generated by the prediction model as a reference track to generate a driving signal and sends the driving signal to the actuator so as to realize accurate control of the actuator. By adopting the gyroscope sensor, the acceleration sensor and the pressure sensor for gait estimation, exoskeleton motion control can be effectively improved, and man-machine interaction experience is effectively improved.

The invention discloses a force feedback exoskeleton and a humanoid robot imitation learning method. The force feedback exoskeleton comprises a bearing part, an arm exoskeleton part and a data glove part. The number of the arm exoskeleton parts is two, the two arm exoskeleton parts are symmetrically connected to the backpack part, and the number of the data glove parts is two so as to collect position and posture information of the left hand and the right hand respectively. The humanoid robot imitation learning method is based on same double-arm characteristics and operator visual angle image input, is combined with nodal topology correlation organization of human joints, integrates multi-modal data information, and outputs control quantity which is almost the same as expert operation. The system is applied to the technical field of exoskeletons, high-quality data acquisition can be realized while the exoskeleton is lightweight and integrated, the number of degrees of freedom of data acquisition of traditional exoskeletons is greatly increased, and the system can be used for guiding collaborative movement of double mechanical arms and further effectively applied to flexible operation tasks of humanoid robots.

The invention discloses a foot drop assisting method based on a flexible ankle joint exoskeleton, and belongs to the field of rehabilitation medical assistance. According to the method, an unhealthy side ankle joint angle is input into a self-adaptive oscillator to learn related parameters in the self-adaptive oscillator, and an affected side ankle joint angle is predicted; meanwhile, a CNP model is adopted to predict the ankle joint angle of the shank on the affected side according to the gait phase estimated by the self-adaptive oscillator, two prediction results of the ankle joint angle of the shank on the affected side are subjected to weighted fusion to finally obtain the expected angle of the ankle joint on the affected side, and the fused ankle joint angle prediction output is more stable and continuous. And a weighted fusion result is smoothed. According to the method, the normal walking mode based on the unhealthy side ankle joint angle of the patient can be dynamically learned online in real time, and personalized affected side ankle joint angle reference tracks at each moment are generated for different patients, so that the abnormal motion mode of the patient is inhibited, and the patient is guided to reach the normal gait in the rehabilitation training process.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

A system includes exoskeleton having at least one inflatable bladder. The exoskeleton is made of fabric / flexible materials, and has flexible (deflated) and rigid (inflated) states. The system's pressurization system operates to pressurize and depressurize the bladder(s). A control system includes a sensor operable to gather data to in relation to movement of a wearer of the exoskeleton. A control module is operable to process data gathered from the sensor(s) and provide a control signal to the pressurization system to cause the pressurization system to selectively pressurize or depressurize the bladder(s) as a function of the data. The control system may be configured to inflate the bladder(s) into the rigid state to provide assistive support when sensor data indicates that the wearer is standing / walking, and to deflate the bladder(s) into the flexible state to allow the wearer to sit / lie comfortably when not standing / walking, and thus when support is not needed.

The invention discloses a brain-computer interface upper limb motion recovery training system, and relates to the technical field of upper limb motion recovery training, the brain-computer interface upper limb motion recovery training system comprises a multi-modal neural signal acquisition module, a combined regulation and control module and a rehabilitation exoskeleton module, the multi-modal neural signal acquisition module is connected with the combined regulation and control module through a wireless Bluetooth protocol, and the combined regulation and control module is connected with the rehabilitation exoskeleton module. The combined regulation and control module is connected with the rehabilitation exoskeleton module through a CAN bus, and the combined regulation and control module is used for synchronously processing fNIRS blood flow signals, tDCS nerve regulation and control parameters and EEG electrophysiological characteristics and generating a closed-loop feedback instruction. By installing the signal fusion unit and the intention analysis unit, time-space complementary fusion of EEG and fNIRS signals is realized, the problem of low precision of traditional single-mode intention recognition is solved, and the motion intention decoding accuracy is improved.

Disclosed is a lightweight hand exoskeleton force feedback apparatus, including a driver, a first rotating link, a second rotating link, a first linkage link, a second linkage link, a finger sleeve, and a pressure sensor fixing member; the driver is worn on a back of metacarpal bone of a human hand, the finger sleeve is fixed on an index finger, and the pressure sensor fixing member is fixed below the index finger; when the human hand bends to simulate a state of grasping an object, the driver drives the first rotating link to couple with the first linkage link and the second linkage link through the second rotating link to drive the finger sleeve to bend and stretch, force feedback is applied to the fingertip, and a pressure is accordingly imposed on a pressure sensor of the pressure sensor fixing member, so that closed-loop force feedback control is implemented.

The invention discloses an intelligent wearable exoskeleton self-adaptive auxiliary regulation and control method and an intelligent wearable exoskeleton system.The intelligent wearable exoskeleton self-adaptive auxiliary regulation and control method comprises the steps that a motion data sample of human walking is obtained, and a human motion model is constructed based on a man-machine closed-chain motion model according to the motion data sample; collecting multi-modal data of the target user when the target user wears the exoskeleton in real time; calling a human body motion model and a man-machine closed-chain motion model, and generating an exoskeleton control instruction of man-machine motion cooperation according to the multi-modal data; and dynamically adjusting motion control parameters of the exoskeleton according to the control instruction. According to the method, the precise human body motion model is constructed based on the man-machine closed-chain motion model, the man-machine motion cooperative exoskeleton control instruction is generated in combination with the model, the exoskeleton motion control parameters are dynamically adjusted according to the instruction, the body type difference, the muscle strength level and the motion habit of different users can be precisely matched, and the wearing comfort and safety are effectively improved.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

Systems and methods related to the operation of wearable robotic systems are disclosed. In one embodiment, a user intention model is developed to accurately identify the intentions of a user in real-time. Utilizing data from multiple wearable sensors, including Inertial Measurement Units (IMUs) and capacitive deformation sensors, a neural network-based approach is employed to capture both kinematic-related and kinetic-related signals. This intention model aims to enhance the accuracy and reliability of movement identification, enabling more effective control of the exoskeleton. In one embodiment, the effects of hysteresis in the exoskeleton system are addressed through the implementation of the Preisach model. By integrating the Preisach model into the control approach, the effects of hysteresis are mitigated, enabling accurate prediction of the required pressure for delivering support to the individual user. By accurately identifying user intentions in real-time and compensating for hysteresis effects, the exoskeleton's performance is optimized.

The embodiment of the invention relates to the technical field of information, and discloses an intelligent data acquisition method and device, a medium and a product, the intelligent data acquisition method comprises the following steps: acquiring artificial demonstration data through an exoskeleton system, the artificial demonstration data comprising video stream data, motion data and force sense data; importing the video stream data into a first large language-vision model, segmenting the video stream data into a plurality of independent task fragments, and generating independent task fragment descriptions; importing the independent task fragment description into a large language model, and generating a task label based on natural language description; and exporting the independent task segments, the task labels and the corresponding motion data and force sense data to generate intelligent data. Therefore, a low-cost, high-efficiency and large-scale intelligent data acquisition and processing method is established, and automatic conversion from human operation data to high-quality VLA training data is realized.

Provided in the present invention are an exoskeleton shared control method, system and device based on AI co-driving, and a storage medium. The exoskeleton shared control method based on AI co-driving comprises: acquiring real-time pose data of an exoskeleton wearer; on the basis of the pose data, performing switching between a local trajectory correction mode and a hybrid long-short-term motion optimization mode in real time, wherein the local trajectory correction mode is completed on the basis of an AI co-driving policy network model; and performing multi-task tracking on the direction of a three-dimensional motion of an exoskeleton and a walking trajectory thereof, so as to achieve motion tracking control over the exoskeleton. By using the method provided in the present application, when encountering a sudden obstacle, an initial path can be corrected to an optimal path in combination with pose data and target point image feature data, thereby helping an exoskeleton to realize omnidirectional walking in a three-dimensional complex environment.

The invention relates to a shoulder two-degree-of-freedom upper limb lifting power-assisted exoskeleton. In order to overcome the defects in the prior art, the shoulder two-degree-of-freedom upper limb lifting assisting exoskeleton comprises a body fixing assembly, two energy storage supporting boxes and two upper arm fixing assemblies, the two energy storage supporting boxes are fixed to a body strap, the two upper arm fixing assemblies are fixed to the upper arms of a user, and the two upper arm fixing assemblies are fixed to the lower arms of the user. The upper arm fixing assembly comprises an upper arm supporting piece and an upper arm binding piece, an arm rod is hinged to the upper arm supporting piece, the lower end of the arm rod is directly or indirectly connected to the adjacent energy storage supporting box, an elastic body is arranged in the energy storage supporting box, an upper rotating seat is arranged at the top of the energy storage supporting box, and a lower rotating seat is arranged at the bottom of the upper rotating seat. The outer side of the shoulder joint piece is hinged to the upper rotating base through a transverse shaft, and the lower end of the arm rod is indirectly connected to the adjacent energy storage supporting box through the shoulder joint piece. According to the shoulder two-degree-of-freedom upper limb lifting power-assisted exoskeleton, left-right arm opening and closing are not limited, and application and popularization of the upper limb lifting power-assisted exoskeleton are facilitated.

The light-weight lower limb exoskeleton control system comprises a backrest assembly, leg assemblies are arranged on the two sides of the backrest assembly, and each leg assembly comprises a hip joint movement mechanism, a thigh exoskeleton mechanism, a shank exoskeleton mechanism and a foot mechanism which are sequentially arranged from top to bottom; a knee joint energy storage mechanism is connected between the thigh exoskeleton mechanism and the shank exoskeleton mechanism; the knee joint energy storage mechanism comprises an upper mounting seat, a lower mounting seat, a telescopic sleeve and an energy storage spring sleeving the telescopic sleeve; the upper mounting seat is fixedly connected with the thigh exoskeleton mechanism and is connected with the upper end of the telescopic sleeve through a rotating pair; the lower mounting seat is fixedly connected with the shank exoskeleton mechanism and is connected with the lower end of the telescopic sleeve; a plurality of mounting holes are formed in the upper mounting seat, and the elastic strength of the knee joint energy storage mechanism is changed according to the elongation of the energy storage spring when the telescopic sleeve is mounted at different positions. Compared with the prior art, the method has the requirements of light weight and low control complexity.

The invention discloses a control method and system of a lower limb exoskeleton power assisting device for iron tower climbing. The method comprises the steps that electromyographic signals are processed, and muscle activation degree indexes are calculated; the two-channel LSTM neural network realizes motion intention online prediction; iron tower cross arm structure edge identification and safe climbing path generation; designing an asymmetric impedance function and optimizing joint stiffness damping parameters; solving a metabolic consumption evaluation model and a minimum energy consumption assistance curve; and real-time calculation is realized through three-stage control period setting and hardware acceleration. Based on the method, all the modules of the exoskeleton power assisting device operate cooperatively; data are accurately collected through the sensor, the power and execution module is flexibly adjusted according to instructions, efficient operation is ensured through the control and operation module, stable electric power is provided through the energy module, and therefore the safety and efficiency of iron tower climbing operation are remarkably improved, and the burden of operators is relieved.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to the technical field of wearable exoskeleton data acquisition and processing, and discloses a wearable exoskeleton data acquisition and processing method and system based on digital twinning, and the method comprises the steps: building a simulation mapping model of exoskeleton joints through employing the digital twinning technology according to upper limb motion data; according to the upper limb motion data, executing a kinematics modeling strategy, calculating an actual pose of the end effector by using a forward kinematics algorithm, and substituting a target pose into inverse kinematics for solving, specifically, obtaining a joint angle, executing a Jacobian matrix modeling strategy, and establishing a model between the actual pose and the joint angle; the influence of the variable quantity of the joint angle on the actual pose of the end effector is obtained; if the pose error is smaller than or equal to an error threshold value, acquiring a joint angle at the moment, and recording the joint angle as a target joint angle; if the pose error is greater than an error threshold value, executing a joint angle tuning strategy, adjusting the joint angle, and repeating the steps S2-S5; the simulation precision is improved, and the man-machine coordination is improved.

The invention discloses a double-clutch variable-stiffness knee joint exoskeleton, and relates to the technical field of wearable lower limb exoskeletons.The double-clutch variable-stiffness knee joint exoskeleton comprises a gait sensing module arranged outside and a limb connecting arm module used for being connected with a human body, and an energy storage module is installed on one side of the limb connecting arm module; a sensor module is mounted on the other side of the limb connecting arm module; the limb connecting arm module comprises a limb connecting arm assembly I used for being connected with the thigh of the human body and a limb connecting arm assembly II used for being connected with the shank of the human body, the limb connecting arm assembly I and the limb connecting arm assembly II are each provided with a clutch unit, and the core speed increaser is installed between the limb connecting arm assembly I and the limb connecting arm assembly II. The energy storage module and the sensor module are arranged on the two sides of the core speed increaser, and the core speed increaser is connected with the energy storage module. The device has the advantages of compact structure, variable rigidity bandwidth and good motion adaptability, can realize high energy recovery efficiency in a multi-gait mode, and reduces the metabolic consumption of a human body.

The utility model discloses an outer skeleton, prefilter and water consuming system relates to prefilter technical field, wherein outer skeleton includes barrel frame and siphon runner, said siphon runner includes the side wall runner of said side frame and is provided the bottom runner of said chassis, said side wall runner is communicated with said bottom runner, said bottom runner is provided with said bottom runner, said side wall runner is provided with said side frame, said bottom runner is provided with said bottom runner, said bottom runner is provided with said bottom runner, said bottom runner is provided with said bottom runner. The side wall flow channel is provided with a siphon hole communicated with the inner cavity of the cylinder frame, and the siphon flow channel is provided with a first discharge port communicated with the outside of the cylinder frame. According to the scheme, the rotatable outer framework and the siphon flow channel are introduced, so that the filtering efficiency and the cleaning effect are improved, the maintenance process is simplified, and more convenient, efficient and economical use experience is brought to a user.

The invention discloses an exoskeleton attitude prediction system for building construction, particularly relates to the field of attitude prediction, and comprises a multi-dimensional sensing module, a data preprocessing module, an attitude prediction model module and a control decision module. A multi-dimensional sensing module collects joint movement, muscle movement and human-computer interaction force data through a nine-axis IMU, a surface myoelectricity sensor and a miniature strain type force sensor; the preprocessing module obtains standardized feature data through filtering, standardization and exception elimination; the attitude prediction model uses a CNN-LSTM hybrid architecture, and outputs a target attitude of 0.5-2 seconds; and the control decision module compares a prediction result with a safety threshold value, generates control instructions in three levels to drive exoskeleton correction, synchronous feedback and recording, solves the problem of response delay of a traditional exoskeleton, and improves the construction safety and efficiency.

The invention relates to the field of rehabilitation instruments, in particular to an intelligent pneumatic hand function rehabilitation training system which comprises a flexible exoskeleton glove provided with a pneumatic muscle array and used for assisting fingers of a user in grasping training. The fingertip position of the glove is connected with a magnetorheological damper through a flexible cable, and the magnetorheological damper is used for providing dynamic resistance in the training process. The electrical stimulation module is used for activating hand muscle groups in a targeted manner through a surface electrode and enhancing neuromuscular control; the sensor module is used for collecting physiological data of a user in real time; the physiological data comprises electromyographic signals and tissue blood oxygen saturation; the control unit is used for switching training modes, by means of the intelligent air pressure hand function rehabilitation training system, the training intensity can be matched with the real-time ability of a patient all the time, and the rehabilitation training effect is improved.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

A trunk-supporting exoskeleton for reducing muscle forces in a wearer's back during forward lumbar flexion comprises a supporting trunk, a first thigh link, a second thigh link, and an actuator, which includes an actuator first element and an actuator second element. When the wearer is in a forward-bent position, the actuator generates a first torque on the actuator first element and a second actuator torque on the actuator second element to generate extension torques between the first and second thigh links and the supporting trunk, thereby resisting bending motion of the supporting trunk in the forward-bent position. When the wearer is not in the forward-bent position, the actuator generates a substantially small first torque and second torque, resulting in small resistance to the movement of the thigh links relative to the supporting trunk during walking.

An exoskeleton system comprising a leg actuator unit that is configured to be coupled to a leg of a user. The leg actuator unit includes: an upper arm and a lower arm that are rotatably coupled via a rotatable joint, the rotatable joint configured to be positioned at a knee of the user with the upper arm coupled about an upper-leg portion of the user above the knee and with the lower arm coupled about a lower-leg portion of the user below the knee. The upper arm is configured to be coupled to the upper-leg portion above the knee via a first set of couplers that includes a first upper-leg coupler, the lower arm is configured to be coupled to the lower-leg portion below the knee via a second set of couplers that includes one or more lower-leg couplers associated with a lower-leg brace, and an actuator extends between the upper arm and lower arm, the actuator configurable to move the upper arm and lower arm.

An approach for assisting users with physical activities by using one or more exoskeleton suits is disclosed. The approach includes, receiving a plurality of data from a user and devices; analyzing one or more problems associated with the plurality of data; determining one or more solutions for the one or more problems based on the analysis; identifying an optimal solution from the one or more solutions; instructing the one or more exoskeleton suits with the optimal solution, wherein the user wears the one or more exoskeleton suits; and performing the instructions, by the exoskeleton suits, based on the optimal solution.