Clinical monitoring system with feedback

Abstract

There is disclosed an exercise monitoring and coaching system which integrates mobile device video and audio capabilities with advanced analysis to deliver real-time feedback and personalized rehabilitation guidance. Patient movements are recorded via a smartphone camera and microphone, then processed by modules that assess posture, range of motion, timing, and signs of discomfort. The system identifies deviations from prescribed exercises, provides immediate corrective cues, and dynamically adjusts exercise parameters. Key innovations include facial expression recognition to detect pain or fatigue, audio- based feedback interpretation, and a multimodal fusion engine that synthesizes video, audio, and sensor data into a single performance profile. This holistic approach addresses the limitations of existing quantitative-only methods by focusing on qualitative movement execution, enabling safer, more effective rehabilitation and supporting patients in real time with minimal hardware requirements.

Description

CLINICAL MONITORING SYSTEM WITH FEEDBACK TECHNICAL FIELD

[0001] The present invention relates to a clinical monitoring system with feedback. In one form the feedback may be in the nature of guidance feedback to a patient . In some forms the guidance may be audio . In other forms the guidance may be by video .

[0002] In an alternative form the feedback may be in the nature of periodic or continuous update of best clinical practice parameters as fed back to the clinical monitoring system.

[0003] In some forms the clinical monitoring system with feedback may be applied in an orthopaedic context . In alternative forms the clinical monitoring system with feedback may be applied in other clinical care contexts including but not limited to cardiac care, and more general physiological care .

[0004] In particular non-limiting forms embodiments of the present invention may relate to methods for monitoring exercise performance and providing coaching feedback based on quantitative assessments of movement execution .

[0005] In particular non-limiting forms embodiments of the present invention may relate to methods for monitoring exercise performance and providing coaching feedback based on qualitative assessments of movement execution .

[0006] The methods may be applied in combination or combinations so as to offer a continuum of care by use of a program that captures the patient at any stage including:Prevention (Prior to any Pre-Hab program, where we divert a patient or delay a patient for surgical input - This is a program that comes prior to Pre-Hab)Pre-Hab ( Pre-Surgery )Post-Op (Post Surgery, occurs after Pre-Hab) - also called Rehab

[0007] In further particular non-limiting forms embodiments of the present invention may relate to orthopaedic recovery and rehabilitation technologies, specifically systems and methods for monitoring exercise performance and providing coaching feedback based on quantitative and qualitative assessments of movement execution .REFERENCE TO PRIORITY APPLICATIONThe text and drawings of the priority application AU 2025900209 filed 26 January 2025 in the name of the same applicant are hereby incorporated by cross reference .BACKGROUND

[0008] Orthopaedic rehabilitation is essential for patients to restore mobility, strength, and functionality following injuries or surgeries . Traditional rehabilitation methods often emphasize quantitative metrics such as range of motion, repetition counts, and step tracking. While these metrics provide valuable data, they frequently overlook qualitative aspects of movement execution, including signs of discomfort, hesitancy, irregular motion patterns, or improper techniques . Neglecting these qualitative factors can impede recovery and may even result in further injury.

[0009] To address these challenges, advancements have been made in Al-based video and audio / speech feedback monitoring technologies that utilize smartphone cameras, microphones, and speakers to observe patient exercises and provide real-time feedback. This approach aims to enhancethe quality of rehabilitation by ensuring exercises are performed correctly and safely.

[0010] Several companies have developed technologies in this domain:

[0011] Kemtai : Offers an Al-based computer vision exercise platform that uses proprietary neural network algorithms to analyze human motion and provide real-time corrective guidance . Their software transforms any device with a camera into an intuitive, real-time guide, enhancing home physiotherapy and rehabilitation sessions .

[0012] OneStep: Provides a digital health platform featuring a smartphone-enabled motion analysis tool . Their system captures objective data to monitor progress and motivate patients with immediate, actionable feedback, facilitating remote therapeutic monitoring without the need for wearables .

[0013] Sency: Utilizes motion capture technology to improve physiotherapy outcomes by offering live feedback during therapy sessions . Their system allows therapists to make immediate corrections if a patient ' s form deviates from the ideal, ensuring exercises are performed correctly to maximize benefits and prevent injury.

[0014] In addition to these companies, several patents closely relate to Al-based video monitoring in physical therapy :

[0015] W02011120121A1 : Describes a physiotherapy animation and rehabilitation management system that provides a library of animated video physical therapy exercises . This system enables healthcare professionals and remote patients to improve conditions such as strength, flexibility, and mobility through guided exercises .

[0016] W02020149815A2 : Details an interactive artificial intelligence application system used in vestibular rehabilitation treatment . The system automates vestibularrehab treatments to enhance compensation and activate adaptation mechanisms in patients suffering from balance disorders .

[0017] US20210060790A1 : Introduces an Al-based robotic system for physical therapy that is responsive to various bodily responses during therapy. The system adjusts physical therapy based on these responses and provides quantifiable feedback on user progress and performance .

[0018] These developments highlight the growing integration of artificial intelligence and computer vision technologies in physical therapy, aiming to provide more comprehensive and qualitative assessments of patient movements . By focusing on both quantitative and qualitative aspects of rehabilitation exercises, these innovations strive to improve patient outcomes and reduce the risk of further injury during the recovery process .

[0019] A further problem relates to availability of clinical data to supervising practitioners . Historically supervising practitioners have had to rely on patient recollection at time of consultation as to the patient' s lived experience leading up to the need for the consultation .

[0020] It would be advantageous if a system could be provided which made available rigorous clinical data derived from a patient' s lived experience when undergoing one or more or all of prevention care or pre-hab care or post-op care in the context of a patient' s lived experience arising from use of a clinical monitoring system with feedback where data is acquired by that system as part of clinical monitoring and supervising of a patient' s lived experience under direction of that system.

[0021] It would be helpful if the data acquired could be obtained as part of a continuum of care offered to the patient and, indeed, a prospective patient .

[0022] It is an object of the present invention to address or at least ameliorate some of the above disadvantages or provide a useful alternative .Notes

[0023] The term "comprising" (and grammatical variations thereof) is used in this specification in the inclusive sense of "having" or "including", and not in the exclusive sense of "consisting only of" .

[0024] The above discussion of the prior art in the Background of the invention, is not an admission that any information discussed therein is citable prior art or part of the common general knowledge of persons skilled in the art in any country.

[0025] Independent claims define the broadest aspects of the invention: a method, a system, and a computer-readable medium, respectively.

[0026] Dependent claims refine and narrow the scope of each independent claim, incorporating additional specific features or functions— such as local inference, gesture-based controls, multi-angle monitoring, or progressive feedback algorithms .

[0027] Alternative embodiments can be embedded in dependent claims or presented as separate sets of claims focusing on specialized features (e . g. , offline operation, local processing, integration with wearable sensors, etc . ) .

[0028] The specific numbering and format of claims may vary based on jurisdictional requirements and drafting preferences .

[0029] These example claims highlight key differentiators of the invention, including facial expression recognition, real-time analysis, audio-based timing feedback, and dynamic exercise ad ustment— all leveraging a standard mobile device' s camera and microphone .SUMMARY OF INVENTIONDefinitions

[0030] Preferred embodiments offer a continuum of care by use of a program that captures the patient at any stage of their care journey under a supervising practitioner including :Prevention; in this specification prevention encompasses a pre pre-hab program where we divert a patient or delay a patient for surgical input . In preferred forms it will occur prior to pre-hab . Most usually it will be instigated as a result of a routine visit to a supervising practitioner where initial and likely very minor ailments are communicated to the supervising practitioner .Pre-Hab; in this specification pre-hab encompasses prerehabilitation or pre-surgery.Post-Op; in this specification post-op or post surgery or rehab or rehabilitation occurs after surgery. In preferred forms it will occur after pre-hab .

[0031] Supervising practitioner : In this specification a "supervising practitioner" means a qualified healthcare professional engaged by a patient or prospective patient to assist in clinically supervised care of that patient or prospective patient . Practitioners may be qualified as physiotherapists, general practitioners or specialist surgeons for example .

[0032] Smartphone : in this specification a smartphone is a particular form of data acquisition and communicationdevice . Current smartphones available on the market include the iPhone 17 marketed by Apple Inc . and the Galaxy 25 marketed by Samsung. These smartphones are noted for their light weight, small form factor and portability. Currently they represent a non-limiting preferred form of data acquisition and communication device for use with embodiments of the clinical monitoring system with feedback described in the present specification. A more general term is "mobile device" which is a device incorporating the salient aspects of the smartphone sufficient to give effect to embodiments of the present invention.Accordingly in one broad form of the invention there is provided a method for providing clinical feedback using a mobile device equipped with a camera and a microphone, the method comprising:a . Acquiring video data of a patient performing one or more prescribed exercises via the camera of the mobile device;b . Acquiring audio data of the patient during the exercises via the microphone of the digital acquisition devicec . Analyzing in real-time, using one or more processing modules, the acquired video data to : i . track the patient' s body movements;ii . evaluate alignment and range of motion; d. Analyzing in real-time, using the one or more processing modules, the acquired audio data to detect timing, effort indicators, and patient cues related to exercise performance;e . Identifying one or more deviations from a prescribed exercise technique based on the real¬ time analysis of the video data and the audio data;f . Generating immediate corrective feedback based on the identified deviations, wherein the corrective feedback is presented to the patient on the mobile device; andg. Adjusting one or more parameters of the patient' s exercise regimen in real-time based on the patient' s performance and any detected indications of discomfort or pain.Preferably the method further comprises providing a user interface on the mobile device that displays a live overlay indicating incorrect posture or improper form by superimposing corrective markers on the video data .Preferably the method further comprises wherein the real-time analysis of facial expressions employs a facial landmark detection algorithm configured to guantify pain or discomfort levels based on facial muscle tension and micro-expressions .Preferably the method further comprises wherein adjusting one or more parameters comprises :Altering the number of repetitions, resistance, or duration of an exercise .Preferably the method further comprises providing alternative exercise variations if indications of discomfort or improper technique are detected.Preferably the method further comprises adapting the frequency and detail level of the corrective feedback according to the patient' s progress, such that beginners receive more frequent and detailed instructions, while patients demonstrating improved proficiency receive higher-level guidance .Preferably the method further comprises providing a step of clinical monitoring and feedback.Preferably the method further comprises including at least one feedback mechanismPreferably the method further comprises the feedback mechanism comprises displaying data on a screen to a patient .Preferably the method further comprises the feedback mechanism comprises displaying a patient dashboard to the patient .Preferably the method further comprises the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .Preferably the method further comprises the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .Preferably the method further comprises wherein the aspects are timing aspects .Preferably the method further comprises wherein the aspects are timing aspects in the form of exercise duration.Preferably the method further comprises wherein the aspects are degree of flex of joints .Preferably the method further comprises wherein the feedback 1A presented on the screen of smartphone 35 is in relation to progress of a pain at rest check activity and wherein this pain at rest clinical pathway is in response to patient input to the systemand is adjusted in real-time to deliver a safe, clinical pathway to the patient .In a further broad form of the invention there is provided a system for delivering rehabilitation feedback via a mobile device, comprising:A mobile device having a camera configured to capture video data of a patient performing rehabilitation exercises and a microphone configured to capture audio data;Memory storing computer-executable instructions;One or more processors configured to execute the computer-executable instructions to :process the captured video data with a movement analysis module to identify deviations from prescribed exercise f orm;process the captured audio data to measure cadence, timing, or effort-related indicators;generate corrective feedback in real-time based on the processed video, audio, ; andpresent the corrective feedback on a user interface of the mobile device; andA feedback adjustment module configured to modify, in real-time, the patient' s exercise parameters or instructions responsive to the patient' s performance data .Preferably the system further comprises a facial expression recognition module comprises a machinelearning model trained on labeled facial expression datasets, enabling detection of subtle expressions correlated with varying levels of pain or exertion .Preferably the system further comprises a multimodal fusion engine configured to combine video-based motion data, audio-based timing cues, and facial expression analysis into a single patient performance profile that informs real-time recommendations .Preferably the system further comprises a module wherein the feedback adjustment module is programmed to escalate alerts to a remote clinician or caregiver if persistent or severe indications of pain are detected during multiple iterations of an exercise .Preferably the system further comprises a step of clinical monitoring and feedback.Preferably the system further comprises at least one feedback mechanismPreferably the feedback mechanism comprises displaying data on a screen to a patient .Preferably the feedback mechanism comprises displaying a patient dashboard to the patient .Preferably the feedback mechanism comprises displaying a practitioner dashboard to a practitioner.Preferably the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .Preferably the aspects are timing aspects .Preferably the aspects are timing aspects in the form of exercise duration .Preferably the aspects are degree of flex of joints .In yet a further broad form of the invention there is provided a non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause a mobile device to perform a method for rehabilitation, the method comprising :Capturing video and audio data of a patient performing prescribed exercises;Analyzing the video data to determine body posture, alignment, and range of motion;Monitoring the audio data to assess timing irregularities or verbal indicators of strain;Producing real-time corrective feedback displayed or spoken via the mobile device; andAdapting the patient' s exercise plan based on the aggregated analysis of movement, and audio cues .Preferably the real-time corrective feedback comprises textual, graphical, or auditory cues emphasizing proper exercise technique and alerting the patient when deviations or indications of discomfort are detected.Preferably the non-transitory computer-readable medium further comprises instructions that cause the mobile device to :Implement local inference for video, audio, and facial expression analysis; andOperate in an offline mode where patient data is processed without reliance on remote cloud servers .Preferably the medium further includes at least one feedback mechanism.Preferably the feedback mechanism comprises displaying data on a screen to a patient .Preferably the feedback mechanism comprises displaying a patient dashboard to the patient .Preferably the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .Preferably the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .Preferably the aspects are timing aspects .Preferably the aspects are timing aspects in the form of exercise duration.Preferably the aspects are degree of flex of joints .Preferably the method or system further comprises enabling gesture-based interaction for patients, wherein the patient can initiate, pause, or navigate through exercise modules using recognized hand or body gestures, thereby reducing reliance on touchscreen input .Preferably the method or system further comprises integrating multiple camera feeds from different angles to improve detection of improper techniques, wherein each camera feed is analyzed in parallel and merged into a single comprehensive motion model .Preferably the method or system further comprises the feedback dynamically transitions from granular, step-by- step instructions to higher-level guidance as patient performance metrics indicate improved proficiency and reduced pain signals .In yet a further broad form of the invention there is provided a clinical monitoring and feedback system which provides a step of clinical monitoring and feedback.Preferably the system further includes at least one feedback mechanismPreferably the system further includes wherein the feedback mechanism comprises displaying data on a screen to a patient .Preferably the system further includes wherein the feedback mechanism comprises displaying a patient dashboard to the patient .Preferably the system further includes wherein the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .Preferably the system further includes wherein the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .

[0033] The invention in yet further preferred forms presents a novel rehabilitation system and method that leverages a mobile device' s camera and microphone to deliver real-time exercise monitoring and personalized coaching . By combining video and audio data streams with analysis, the system identifies both quantitative parameters— such as repetitions and range of motion— and qualitative indicators of performance, including improper alignment, irregular timing suggesting fatigue or pain, and facial expressions suggesting discomfort or pain.

[0034] In some contexts the system is applied to orthopaedic patients or prospective orthopaedic patients .

[0035] Core features include a movement analysis module to detect deviations in posture, a timing measurement module to evaluate rhythm and consistency, and a facial expression recognition module to interpret signs of strain or distress . The system also may include an intelligent verbal feedback module that enables patients to verbally interact with the system. The system immediately provides corrective feedback and dynamically adjusts exercise parameters based on each patient' s condition and progress . This continuous feedback loop, aided by natural language processing, ensures patients receive tailored, therapist-grade guidance throughout their recovery.

[0036] Designed for use with standard smartphones, embodiments of the invention are accessible and cost- effective, removing the need for specialized equipment . By integrating real-time, multimodal analysis into one platform, the invention significantly enhances the quality of orthopaedic rehabilitation, helping patients recover safely, effectively, and with personalized care .BRIEF DESCRIPTION OF DRAWINGS

[0037] Embodiments of the present invention will now be described with reference to the accompanying drawings wherein :

[0038] Figure 1A is a System Architecture Diagram which illustrates the integration of video and audio inputs, analysis modules (movement analysis, facial expression recognition, timing measurement) , and the feedback loop to the patient .

[0039] Figure 1B1 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback applied in a prevention context .

[0040] Figure 1B2 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback applied in a prehab context .

[0041] Figure 1B3 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback applied in a post-op context .

[0042] Figure 2 is an Analysis Workflow which shows the sequential process of data acquisition, multimodal analysis, detection of patient discomfort, and real-time regimen adj ustment .

[0043] Figure 3 is an Example patient interaction which illustrates an example interaction between the patient and the system, demonstrating the process of selecting an exercise routine, capturing data during execution, providing real-time corrective feedback, and generating a performance summary for clinician review.

[0044] Fig 4A illustrates in flow chart form the detection and preparation phase of an exercise presented to a patient by means of the smartphone 35.

[0045] Fig 4B illustrates in flow chart form the ready state phase of an exercise presented to a patient by means of the smartphone 35.

[0046] Fig 4C illustrates in flow chart form the in progress state phase of an exercise presented to a patient by means of smartphone 35.

[0047] Fig 5 illustrates the feedback 1A presented on the screen of smartphone 35 in relation to progress of a knee range of motion check activity.

[0048] Fig 6 illustrates the feedback 1A presented on the screen of smartphone 35 in relation to progress of a pain at rest check activity.

[0049] Fig 7 illustrates the feedback 1A provided by smartphone 35 as part of a pain during exercise - moderate pain detected analysis .

[0050] Fig 8 is a flow chart of analysis of feedback 1A provided by a patient during exercise .

[0051] Fig 9 illustrates the feedback 1A provided to the patient by smartphone 35 as part of the pain with swelling during exercise analysis .

[0052] Figure 10 is an example of a layout of a patient dashboard

[0053] Figure 11 is an example of feedback type IB presented visually to a patient

[0054] Figure 12 illustrates a further example of feedback type 2 that may be presented to a supervising practitioner

[0055] Figure 13 illustrates a further example of feedback type 2 that may be presented to a supervising practitioner

[0056] Figure 14 illustrates a further example of feedback type 2 that may be presented to a supervising practitioner .DESCRIPTION OF EMBODIMENTS

[0057] Example embodiments will now be described more fully with reference to the accompanying drawings . Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art . The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments . In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure . One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like . In other instances, well- known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure .

[0058] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale . The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities . These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in differentnetworks and / or processor devices and / or microcontroller devices .First Preferred Embodiment

[0059] With reference to figure 1A the system architecture for the orthopaedic remediation tool is designed to seamlessly integrate video 15 and audio 20 inputs with analysis modules 25 to provide dynamic and real-time therapeutic feedback to patients 30. The system relies on a smartphone 35 or similar device, equipped with a high-resolution camera 15 and microphone 20, to capture video and audio data of the patient performing prescribed exercises . These inputs serve as the foundational data streams for analysis, enabling the system to observe the patient' s movements, detect subtle facial expressions 40 while listening to verbal feedback from the patient, and capture timing patterns 45 associated with each exercise .

[0060] Upon acquisition, the video and audio data are transmitted over a network such as the Internet 46 to infrastructure 47 containing the analysis modules 25, where they undergo multimodal processing. The movement analysis module 50 evaluates the patient' s 30 posture, form, and range of motion to ensure that the exercises are being performed correctly. It 50 identifies deviations from prescribed techniques and highlights potential areas of concern, such as improper alignment or restricted motion. Simultaneously, the facial expression recognition module 55 analyses the patient' s facial cues 40, identifying expressions that may indicate pain, discomfort, or emotional distress . This capability allows the system to gauge the patient' s emotional and physical state beyond their movement patterns alone .

[0061] In parallel, the timing measurement module 60 monitors the cadence and rhythm of the exercises, detectinghesitation, irregular timing, or prolonged pauses that could signify difficulty, fatigue, or pain. By combining these streams of analysis, the system generates a comprehensive understanding of the patient' s performance in real time .

[0062] The analysed data is then processed by a central decision-making module 65, which interacts with a natural language processing system 70 to generate therapist-grade feedback. This feedback is tailored to the patient' s immediate needs and delivered in a conversational and supportive manner . If significant issues are- detected, such as improper technique or signs of discomfort, the system dynamically adjusts the exercise regimen. Adjustments may include modifications to the intensity, duration, or type of exercise, ensuring that the regimen remains aligned with the patient' s capabilities and therapeutic goals .

[0063] The feedback loop is completed as the adjusted instructions and guidance are communicated back 75 to the patient in real time via the device' s interface 80. This continuous interaction enables the system to emulate the expertise and responsiveness of a human therapist, providing a personalized and effective rehabilitation experience .Feedback IB via dashboard 85With reference to fig 10 there is illustrated a dashboard layout as would be presented on patient dashboard 85 in this instance for the purpose of monitoring chronic disease as may happen as part of a prevention treatment plan.Feedback IB via dashboard 85With reference to fig 11 there is illustrated feedback IB that may be presented to a patient as part of a rehab treatment plan in accordance with the embodiment of figure 1B3 .Feedback 2 via dashboard 90With reference to fig 12 there is illustrated feedback 2 that may be presented to a supervising practitioner in this instance monitoring the progress of a patient during a rehab phase of treatmentThe supervising practitioner may be presented with further detailed dashboards as for example in fig 13 and fig 14 thereby to provide detailed and consistent clinical data as regards to the progress of the patient . It will be observed that this level of clinical data and accuracy of clinical data will not be obtained merely by interrogating the patient periodically at the time of consultation.Clinical Data Feedback 3

[0064] Figure 1B1 is a System Architecture Diagram of an alternative preferred embodiment of the system incorporating feedback including clinical data feedback applied in a prevention context where like components are numbered as for the embodiment of fig 1A.

[0065] In this instance the system is applied in a prevention context .

[0066] Feedback 1A is provided to the patient by way of visual and audio output from the smartphone 35 during exercise .

[0067] Feedback IB is provided to the patient by way of a patient dashboard 85 which may be presented on thesmartphone 35 typically post exercise . Feedback IB may also be provided via a personal computer or like device in communication with infrastructure 47.

[0068] Feedback 2 is provided via a practitioner dashboard 90.

[0069] Feedback 3 is in the form of communication of current best practice parameters 95 stored on clinical data server 96. These parameters 95 are periodically downloaded to infrastructure 47 as feedback 3. The parameters 95 updated are then incorporated into the settings of the exercises and related activities communicated to the patient via smartphone 35 during exercise . In this manner the patient will always be guided by up to date clinical best practice . For example clinical best practice may change whereby at one point it is recommended that a particular activity or exercise be performed for 10 minutes whereas subsequent clinical best practice may be performed for 15 minutes . The update to 15 minutes will be communicated via feedback 3 as soon as it becomes part of current best practice . This ensures a high level of patient care according to current best practice .

[0070] Figure 1B2 is a System Architecture Diagram of an alternative preferred embodiment of the system incorporating feedback including clinical data feedback applied in a prehab context where like components are numbered as for the embodiment of fig 1A and otherwise described with reference to figure 1B1.

[0071] Figure 1B3 is a System Architecture Diagram of an alternative preferred embodiment of the system incorporating feedback including clinical data feedback applied in a post-op context where like components are numbered as for the embodiment of fig 1A and otherwise described with reference to figure 1B1 .

[0072] With reference to figure 2 the Analysis Workflow discloses a process that shows key steps in an interactive session with the patient and the system. The process begins 100 with the patient starting their session and engaging with it to initiate a therapeutic session. The system enters an iterative coaching and monitoring loop 105. Next the process begins data acquisition 110, where the smartphone' s camera captures high-resolution video and its microphone records audio of the patient performing prescribed rehabilitation exercises . These inputs provide the foundational data needed for analysis . The system automatically transfers this data to the processing modules 115, initiating a comprehensive assessment .

[0073] The movement analysis module 120 evaluates the patient' s posture, form, and range of motion. It identifies deviations 135 from the prescribed techniques, such as restricted motion or improper alignment, ensuring exercises are performed correctly to maximize their therapeutic benefit . Simultaneously, the facial expression recognition module 125 processes the video feed to detect subtle cues 140 indicating discomfort, pain, or emotional distress . Advanced machine learning algorithms analyze facial landmarks to interpret these expressions in real time .

[0074] In parallel, the timing measurement module 130 examines the rhythm, cadence, and consistency of the exercises . It identifies irregularities 145 such as hesitation, prolonged pauses, or abnormal speeds, which could signal fatigue, difficulty, or improper execution. These analyses combine 150 to form a comprehensive understanding of the patient' s performance .

[0075] The outputs from these modules are integrated within a central decision-making engine 155. This engineevaluates the combined data streams to detect issues and dynamically adjusts 160 the patient' s regimen. Adjustments may include modifications to the intensity, duration, or type of exercises, ensuring alignment with the patient' s capabilities and therapeutic goals .

[0076] Real-time, personalized feedback is generated 165 through a natural language processing system. Delivered via text, voice, or visual cues on the smartphone interface, this feedback provides clear, actionable guidance 170. The system continuously loops through this process, enabling iterative coaching and monitoring throughout the rehabilitation session, offering an experience comparable to having a dedicated therapist on hand in real time .

[0077] With reference to figure 3 the figure discloses an example case of a patient recovering from knee surgery who initiates a rehabilitation session 200 using the orthopaedic exercise monitoring system. Upon opening the mobile application 205, the system prompts the patient to select their prescribed exercise routine 210. The patient selects a leg stretch routine and begins the session.

[0078] The smartphone' s camera and microphone activate 215 to capture video and audio data as the patient performs the exercises . The system provides a demonstration 220 of the correct technique before monitoring the patient' s execution 225 in real time . The movement analysis module evaluates the patient' s posture and form 230, detecting deviations 235 such as a rounded back or locked knee . Based on this analysis, the system delivers corrective feedback 240, instructing the patient to adjust their alignment for optimal performance .

[0079] As the session progresses 245, the timing module monitors the cadence and rhythm 250 of the exercises . When irregularities such as extended pauses between repetitionsare detected 255, the system generates a prompt suggesting a steady rhythm 260. The patient adjusts accordingly, enhancing consistency 265.

[0080] During one exercise, the facial expression recognition module identifies signs of discomfort 270, such as a grimace . In response, the system temporarily pauses the session 280 and provides options 285 to modify the exercise intensity or switch to an alternative activity. The patient selects a less intensive variation and continues 290 without further issues .

[0081] At the conclusion of the session 290, the system generates a performance summary 300. The report includes metrics such as the number of repetitions completed, form accuracy, and timing consistency, as well as recommendations for improvement . The summary is saved for clinician review 305 to facilitate ongoing monitoring and personalised adjustments to the patient' s rehabilitation plan .

[0082] This interaction demonstrates the system' s capability to dynamically monitor, analyse, and adapt to the patient' s needs in real time, ensuring effective and safe execution of rehabilitation exercises .Examples of system with patient and exercise feedbackSquat ExerciseWith reference to fig 4A there is illustrated in flow chart form the detection and preparation phase of the exercise presented to a patient by means of the smartphone 35.With reference to fig 4B there is illustrated in flow chart form the ready state phase of the exercise presented to a patient by means of the smartphone 35.With reference to fig 4C there is illustrated in flow chart form the in progress state phase of the exercise presented to a patient by means of smartphone 35.Knee Range of Motion CheckFig 5 illustrates the feedback 1A presented on the screen of smartphone 35 in relation to progress of a knee range of motion check activity.Pain at Rest CheckFig 6 illustrates the feedback 1A presented on the screen of smartphone 35 in relation to progress of a pain at rest check activity.This pain at rest clinical pathway is in response to patient input to the system and is adjusted in real-time to deliver a safe, clinical pathway to the patient . In any other standard pathway the patient typically waits for their next clinical appointment, inperson to get this program adjustment, otherwise delivered in Fig 6 .Pain During Exercise Analysis and FeedbackFig 7 illustrates the feedback 1A provided by smartphone 35 as part of a pain during exercise - moderate pain detected analysis .Fig 8 is a flow chart of analysis of feedback 1A provided by a patient during exercise .Fig 9 illustrates the feedback 1A provided to the patient by smartphone 35 as part of the pain with swelling during exercise analysis .Examples of system with clinical data update feedback .

[0083]

[0084] Figure 1B1 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback 3 applied in a prevention context .

[0085] Figure 1B2 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback 3 applied in a prehab context .

[0086] Figure 1B3 is a System Architecture Diagram of a preferred embodiment of the system incorporating feedback including clinical data feedback 3 applied in a post-op context .Embodiments of the system may be used repeatedly for Pre Pre Rehab, Pre Rehab (ie Pre op) and Rehab (ie post op) individually and also combined consecutively - as follows :The clinical monitoring system 10, 11, 12, 13 with feedback according to the various embodiments as described above may be used for prevention (Pre Pre Rehab) , Pre Rehab (ie Pre op) and Post-op (rehab) individually and also combined consecutively - as follows :2nd embodiment Prevention System (pre pre hab) - 11Previously described embodiments of the system described applications of the system in a rehabilitation context . Inthis second embodiment with reference to fig 1B1 the system of the first embodiment is applied in a pre pre rehabilitation context .People may experience mild aches and pains which they report to their doctor . For example a patient might report very mild pain with the knee joints . In this context it would be far too early to embark on a knee operation. The doctor may suggest diagnostic scans to determine what might be the origin of the mild pain. Other than that the doctor has limited data to go on .In this context it may be helpful if the patient could embark on a clinically supported data gathering exercise during a period which may be described as the "pre rehabilitation" context- sometimes shortened to "prehab" in the art .Embodiments of this system with reference to Fig 1 and the flow chart of Fig 2 may be applied such that the patient may engage in a data gathering exercise in their home environment - An exercise which will generate clinically useful data arising from a supervised movement regime .3rd embodiment- prehab system - 12Previously described embodiments of the system described applications of the system in a rehabilitation context . In this second embodiment with reference to 1B2 the system of the first embodiment is applied in a pre rehabilitation context - - which is to say in an immediately preceding operation context .Where a patient has been determined as needing an operation to replace or repair a joint- for example a knee joint the patient will have some initial monitoring by their doctor and their specialists in the lead up to the day of the operation. Historically this monitoring may simply take the form of a meeting with the doctor or the specialist periodically where the patient can report verbally how the systems are progressing or deteriorating.It would be helpful if a doctor or specialist could receive clinically useful data arising from a supervised movement regime in this period leading up to the actual operation .The previously described embodiments of the system may be applied to provide the clinically useful data and assist in the preparation of the patient for the operation by having the patient participate in a clinically supervised exercise regime .The system described with reference to Fig 1B2 and the flow chart of Fig 2 or other described flow charts may be applied during this pre hab or pre-op period (also known as the pre rehabilitation period) .4thembodiment post-op (rehabilitation)With reference to fig 1B3 and fig 10 a patient may undergo various exercises and activities as outlined in earlier embodiments . Feedback IB in the form of progress of their recovery and any setbacks is communicated to the patient by means of a patient dashboard 85 as illustrated in fig 10.5th embodiment-combined prevention, prehab and rehab systemIn this embodiment with reference to figures 1B1, 1B2, 1B3, the above described pre rehab, prehab and rehab systems areapplied consecutively starting from when a patient first presents to their doctor with the first indications of joint problems . The application of the three systems consecutively may have the benefit of improved data availability for the trading doctor and trading specialist . Research also indicates that exercise supervised by a physiotherapist leading up to an operation maybe a benefit in recovery after the operation. The present system seeks to provide that exercise in the home environment and without requiring a physiotherapist to be present during that exercise .Alternative Embodiments

[0087] In this specification a smartphone is a particular form of data acquisition and communication device . Current smartphones available on the market include the iPhone 17 marketed by Apple Inc . and the Galaxy 25 marketed by Samsung. These smartphones are noted for their light weight, small form factor and portability. Currently they represent a non¬ limiting preferred form of data acquisition and communication device for use with embodiments of the clinical monitoring system with feedback described in the present specification.

[0088] It is envisaged that other data acquisition and communication devices may be utilised to effect embodiments of the above described clinical monitoring system with feedback. In preferred forms they will possess the three characteristics of light weight, small form factor and portability. Possible candidates may include smart spectacles and smart watches .

[0089] Some embodiments of this system have been described with reference to use of Al Processing Modules, more particularly with the assistance of a natural language processing system 70 (refer fig 1) . In alternative embodiments Boolean logic may be utilized to effect therequired functions including facial expression 125, detecting discomfort or emotional distress 140, identity deviation 150 and the like .

[0090] In addition to the primary embodiment leveraging a smartphone' s video and audio capabilities, alternative embodiments of the invention can be envisioned to enhance versatility, accessibility, and functionality in various therapeutic contexts . These embodiments aim to address specific use cases and leverage different technological capabilities while maintaining the core objectives of providing qualitative and quantitative feedback for orthopaedic recovery.

[0091] Facial Recognition and Patient Interaction: A priority for this invention is the integration of facial recognition technology to detect signs of discomfort, emotional distress, or fatigue in real-time . Beyond passive monitoring, the system will actively engage patients through direct queries and responses . For instance, the system may ask patients about their pain levels or difficulty in performing exercises and adapt feedback based on their input . This interactive capability ensures that the system accounts for subjective experiences, providing a more personalized and effective rehabilitation experience . To achieve this, the following modules are integrated:a . Facial Expression Analysis Module : Detects and interprets facial landmarks and expressions indicative of discomfort, pain, or fatigue using advanced algorithms .b. Natural Language Understanding (NLU) Module :Processes patient queries and responses, enablingconversational interactions for a more dynamic user experience .c . Dialogue Management System: Manages the conversational flow, ensuring coherent and contextually appropriate exchanges with the patient .d. Personalization Layer : Adapts feedback and interactions based on the patient' s responses, historical performance, and real-time behavior, creating a tailored rehabilitation experience .

[0092] On-Device Processing: This embodiment employs local capabilities on smartphones, utilizing specialized chips now integrated into modern devices . By processing video and audio data directly on the device, the system ensures real-time feedback without reliance on cloud infrastructure . This approach enhances privacy by eliminating the need to transmit sensitive patient data and enables seamless operation in areas with limited or no internet connectivity.

[0093] Gesture-Based Interaction: Rather than relying solely on touchscreen or voice commands, this embodiment incorporates gesture recognition capabilities to interact with the system. Patients can use hand movements or body gestures to navigate menus, start exercises, or request feedback. This functionality is particularly useful for individuals with limited mobility or in scenarios where touch interaction is impractical .

[0094] Multimodal Data Fusion: This embodiment expands the system' s functionality by integrating additional data streams from built-in smartphone sensors, such asaccelerometers, gyroscopes, and barometers . By fusing this data with video and audio inputs, the system provides a more comprehensive analysis of exercise performance, including detecting subtle shifts in balance or changes in physical effort .

[0095] Progressive Feedback Algorithms : This embodiment introduces adaptive algorithms that adjust the feedback intensity and frequency based on the patient' s progress . For example, the system might start with detailed, step-by- step instructions for beginners and transition to higher- level guidance as the patient demonstrates improved proficiency. This personalized approach supports long-term engagement and adherence to rehabilitation plans .

[0096] Offline Rehabilitation Sessions : For patients in remote areas or with limited internet access, this embodiment supports fully offline operation. The system uses preloaded models and locally stored exercise routines to provide monitoring and feedback. Regular updates to the models can be applied during periodic connectivity, ensuring the system remains current without continuous online dependency.

[0097] Real-Time Multi-Angle Monitoring: This embodiment employs multiple smartphones or cameras within a patient' s environment to capture exercises from different angles . The system integrates these feeds locally, providing a more detailed analysis of posture and movement from various perspectives . This setup enhances accuracy in identifying improper techniques or misalignments .

[0098] Facial Expression and Audio Synthesis : Instead of relying exclusively on visual cues, this embodiment incorporates synthesized audio prompts and dynamic facial expression analysis directly on the smartphone . By utilizing local processing, the system detects subtle signs of discomfort or strain in real-time and provides verbal coaching cues tailored to the patient' s current state .

[0099] These alternative embodiments focus on enhancing functionality through localized capabilities and leveraging the evolving hardware and software potential of modern smartphones, ensuring the system remains adaptable and efficient for diverse rehabilitation scenarios .INDUSTRIAL APPLICABILITY[000100] The system is particularly beneficial for patients recovering from surgeries such as joint replacements, as it provides precise clinical monitoring and feedback to ensure proper execution of prescribed exercises . It also supports individuals with chronic orthopaedic conditions, enabling them to manage their rehabilitation exercises effectively. For athletes recovering from injuries, the system offers advanced movement analysis to enhance recovery outcomes and reduce the risk of reinjury. The system may also be applied with benefit to patients undergoing one or more or all of prevention care, pre-hab care and post-op care .The system and methods may be applied in combination or combinations so as to offer a continuum of care by use of a program that captures the patient at any stage of care .

Claims

CLAIMS1 A method for providing clinical feedback using a mobile device equipped with a camera and a microphone, the method comprising :a . Acquiring video data of a patient performing one or more prescribed exercises via the camera of the mobile device;b . Acquiring audio data of the patient during the exercises via the microphone of the digital acquisition devicec . Analyzing in real-time, using one or more processing modules, the acquired video data to : d. track the patient' s body movements;e . evaluate alignment and range of motion;f . Analyzing in real-time, using the one or more processing modules, the acquired audio data to detect timing, effort indicators, and patient cues related to exercise performance;i . Identifying one or more deviations from a prescribed exercise technique based on the real-time analysis of the video data and the audio data;g. Generating immediate corrective feedback based on the identified deviations, wherein the corrective feedback is presented to the patient on the mobile device; andh. Adjusting one or more parameters of the patient' s exercise regimen in real-time based on the patient' s performance and any detected indications of discomfort or pain.

2. The method of claim 1, further comprising providing a user interface on the mobile device that displays a live overlay indicating incorrect posture or improperform by superimposing corrective markers on the video data .

3. The method of claim 1, wherein the real-time analysis of facial expressions employs a facial landmark detection algorithm configured to quantify pain or discomfort levels based on facial muscle tension and micro-expressions .4 . The method of claim 1, wherein adjusting one or more parameters comprises :a . Altering the number of repetitions, resistance, or duration of an exercise; andb . Providing alternative exercise variations if indications of discomfort or improper technique are detected5. The method of claim 1, further comprising adapting the frequency and detail level of the corrective feedback according to the patient' s progress, such that beginners receive more frequent and detailed instructions, while patients demonstrating improved proficiency receive higher-level guidance .

6. The method of any previous claim further providing a step of clinical monitoring and feedback.7 . The system of any previous claim further including at least one feedback mechanism8 . The system of any previous claim wherein the feedback mechanism comprises displaying data on a screen to a patient .

9. The system of any previous claim wherein the feedback mechanism comprises displaying a patient dashboard to the patient .

10. The system of any previous claim wherein the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .

11. The system of any previous claim wherein the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .12 . The system of any previous claim wherein the aspects are timing aspects .

13. The system of any previous claim wherein the aspects are timing aspects in the form of exercise duration .

14. The system of any previous claim wherein the aspects are degree of flex of joints .15 The system of any previous claim wherein the feedback 1A presented on the screen of smartphone 35 is in relation to progress of a pain at rest check activity and wherein this pain at rest clinical pathway is in response to patient input to the system and is adjusted in real-time to deliver a safe, clinical pathway to the patient .16 A system for delivering rehabilitation feedback via a mobile device, comprising:a. A mobile device having a camera configured to capture video data of a patient performing rehabilitation exercises and a microphone configured to capture audio data;b . Memory storing computer-execut ble instructions ; c . One or more processors configured to execute the computer-executable instructions to :i . process the captured video data with a movement analysis module to identify deviations from prescribed exercise form;ii . process the captured audio data to measure cadence, timing, or effort-related indicators ;iii . generate corrective feedback in real-time based on the processed video, audio, ; and iv. present the corrective feedback on a user interface of the mobile device; and d. A feedback adjustment module configured to modify, in real-time, the patient' s exercise parameters or instructions responsive to the patient' s performance data.The system of claim 6, wherein a facial expression recognition module comprises a machine learning model trained on labeled facial expression datasets, enabling detection of subtle expressions correlated with varying levels of pain or exertion.The system of claim 6, further comprising a multimodal fusion engine configured to combine video-based motion data, audio-based timing cues, and facial expression analysis into a single patient performance profile that informs real-time recommendations .The system of claim 6, wherein the feedback adjustment module is programmed to escalate alerts to a remote clinician or caregiver if persistent or severe indications of pain are detected during multiple iterations of an exercise .The system of any previous claim providing a step of clinical monitoring and feedback.the system of any previous claim further including at least one feedback mechanismThe system of any previous claim wherein the feedback mechanism comprises displaying data on a screen to a patient .The system of any previous claim wherein the feedback mechanism comprises displaying a patient dashboard to the patient .The system of any previous claim wherein the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .The system of any previous claim wherein the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .The system of any previous claim wherein the aspects are timing aspects .The system of any previous claim wherein the aspects are timing aspects in the form of exercise duration. The system of any previous claim wherein the aspects are degree of flex of joints .A non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause a mobile device to perform a method for rehabilitation, the method comprising :e . Capturing video and audio data of a patient performing prescribed exercises;f . Analyzing the video data to determine body posture, alignment, and range of motion;g. Monitoring the audio data to assess timing irregularities or verbal indicators of strain; h. Producing real-time corrective feedback displayed or spoken via the mobile device; andi . Adapting the patient' s exercise plan based on the aggregated analysis of movement, and audio cues . The non-transitory computer-readable medium of claim 29, wherein the real-time corrective feedback comprises textual, graphical, or auditory cues emphasizing proper exercise technique and alerting the patient when deviations or indications of discomfort are detected.The non-transitory computer-readable medium of claim 29 or 30, further comprising instructions that cause the mobile device to :j . Implement local inference for video, audio, and facial expression analysis; andk. Operate in an offline mode where patient data is processed without reliance on remote cloud servers .the medium of any previous claim further including at least one feedback mechanismThe medium of any previous claim wherein the feedback mechanism comprises displaying data on a screen to a patient .The medium of any previous claim wherein the feedback mechanism comprises displaying a patient dashboard to the patient .The medium of any previous claim wherein the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .The medium of any previous claim wherein the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a systemserver thereby to update aspects of the exercise regime .The medium of any previous claim wherein the aspects are timing aspects .The medium of any previous claim wherein the aspects are timing aspects in the form of exercise duration. The medium of any previous claim wherein the aspects are degree of flex of joints .A method (or system) according to any previous claim further comprising enabling gesture-based interaction for patients, wherein the patient can initiate, pause, or navigate through exercise modules using recognized hand or body gestures, thereby reducing reliance on touchscreen input .A method (or system) according to any previous claim, further comprising integrating multiple camera feeds from different angles to improve detection of improper techniques, wherein each camera feed is analyzed in parallel and merged into a single comprehensive motion model .A method (or system) according to any previous claim wherein the feedback dynamically transitions from granular, step-by-step instructions to higher-level guidance as patient performance metrics indicate improved proficiency and reduced pain signals .A method for providing real-time orthopedic rehabilitation feedback using a mobile device equipped with a camera and a microphone, the method comprising:

1. Acquiring video data of a patient performing one or more prescribed rehabilitation exercises via the camera of the mobile device;m. Acquiring audio data of the patient during the exercises via the microphone of the mobile device ;n. Analyzing in real-time, using one or more processing modules, the acquired video data to : i . track the patient' s body movements;ii . evaluate alignment and range of motion; iii . verbal expressions and feedback and other verbal reactions indicative of discomfort or pain; andiv. delays or hesitation in the timing of movement that may indicate pain, tiredness or discomfort .o . Analyzing in real-time, using the one or more processing modules, the acquired audio data to detect timing, effort indicators, and patient cues related to exercise performance;p . Identifying one or more deviations from a prescribed exercise technique based on the realtime analysis of the video data and the audio data;q. Generating immediate corrective feedback based on the identified deviations, wherein the corrective feedback is presented to the patient on the mobile device; andr. Adjusting one or more parameters of the patient' s exercise regimen in real-time based on the patient' s performance and any detected indications of discomfort or pain.The method of claim 43, further comprising providing a user interface on the mobile device that displays a live overlay indicating incorrect posture or improper form by superimposing corrective markers on the video data .The method of any previous claim wherein the real-time analysis of facial expressions employs a facial landmark detection algorithm configured to quantify pain or discomfort levels based on facial muscle tension and micro-expressions .The method of any previous claim wherein adjusting one or more parameters comprises :s . Altering the number of repetitions, resistance, or duration of an exercise; andt . Providing alternative exercise variations if indications of discomfort or improper technique are detectedThe method of any previous claim, further comprising adapting the frequency and detail level of the corrective feedback according to the patient' s progress, such that beginners receive more frequent and detailed instructions, while patients demonstrating improved proficiency receive higher-level guidance .A system for delivering orthopedic rehabilitation feedback via a mobile device, comprising:u. A mobile device having a camera configured to capture video data of a patient performing rehabilitation exercises and a microphone configured to capture audio data;v. Memory storing computer-executable instructions; w. One or more processors configured to execute the computer-executable instructions to :i . process the captured video data with a movement analysis module to identify deviations from prescribed exercise form;ii . process the captured audio data to measure cadence, timing, or effort-related indicators ;iii . apply a facial expression recognition module to detect expressions indicative of pain or discomfort ;iv. generate corrective feedback in real-time based on the processed video, audio, and facial expression data; andv. present the corrective feedback on a user interface of the mobile device; and x. A feedback adjustment module configured to modify, in real-time, the patient' s exercise parameters or instructions responsive to the patient' s performance data and any detected discomfort signals .The system of claim 48 , wherein the facial expression recognition module comprises a machine learning model trained on labeled facial expression datasets, enabling detection of subtle expressions correlated with varying levels of pain or exertion.The system of claim 48 or 49, further comprising a multimodal fusion engine configured to combine videobased motion data, audio-based timing cues, and facial expression analysis into a single patient performance profile that informs real-time recommendations .The system of any previous claim, wherein the feedback adjustment module is programmed to escalate alerts to a remote clinician or caregiver if persistent or severe indications of pain are detected during multiple iterations of an exercise .A non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause a mobile device to perform a method for orthopedic rehabilitation, the method comprising:y. Capturing video and audio data of a patient performing prescribed exercises;z . Analyzing the video data to determine body posture, alignment, and range of motion;aa . Applying a facial expression recognition algorithm to detect discomfort signals from the patient' s facial cues;bb . Monitoring the audio data to assess timing irregularities or verbal indicators of strain; cc . Producing real-time corrective feedback displayed or spoken via the mobile device; and dd. Adapting the patient' s exercise plan based on the aggregated analysis of movement, facial expressions, and audio cues .The non-transitory computer-readable medium of claim 52, wherein the real-time corrective feedback comprises textual, graphical, or auditory cues emphasizing proper exercise technique and alerting the patient when deviations or indications of discomfort are detected.The non-transitory computer-readable medium of claim 52 or 53, further comprising instructions that cause the mobile device to :ee . Implement local inference for video, audio, and facial expression analysis; andff . Operate in an offline mode where patient data is processed without reliance on remote cloud servers .A method (or system) according to any previous claim further comprising enabling gesture-based interaction for patients, wherein the patient can initiate, pause, or navigate through exercise modules using recognized hand or body gestures, thereby reducing reliance on touchscreen input .A method any previous claim further comprising integrating multiple camera feeds from different angles to improve detection of improper techniques, wherein each camera feed is analyzed in parallel and merged into a single comprehensive motion model .A method (or system) according to any previous claim wherein the feedback dynamically transitions from granular, step-by-step instructions to higher-level guidance as patient performance metrics indicate improved proficiency and reduced pain signals .A clinical monitoring and feedback system which provides a step of clinical monitoring and feedback. The system of claim 58 further including at least one feedback mechanismThe system of claim 58 or 59 wherein the feedback mechanism comprises displaying data on a screen to a patient .The system of any previous claim wherein the feedback mechanism comprises displaying a patient dashboard to the patient .The system of any previous claim wherein the feedback mechanism comprises displaying a practitioner dashboard to a practitioner .The system of any previous claim wherein the feedback mechanism comprises feeding back current best practice parameters from a clinical data server to a system server thereby to update aspects of the exercise regime .The system of any previous claim wherein the aspects are timing aspects .The system of any previous claim wherein the aspects are timing aspects in the form of exercise duration. The system of any previous claim wherein the aspects are degree of flex of joints .The system of any previous claim wherein the feedback 1A presented on the screen of smartphone 35 is in relation to progress of a pain at rest check activity and wherein this pain at rest clinical pathway is in response to patient input to the system and is adjusted in real-time to deliver a safe, clinical pathway to the patient .

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