Remote Monitoring and Analysis of Gait and Falls Within an Elderly Population

Study Overview
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Brief Summary
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The investigators aim to do this initial pilot study as an observational prospective cohort study, evaluating elderly patients who have capacity in National Health Service (NHS) rehabilitation and community hospitals. The patients will each be recorded doing simple activities of daily living in two 2 hour sessions using a discrete wireless device. This will generate anonymous data set that can be used to train and refine our machine learning algorithm.

Detailed Description
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Phase 1 - Creation The creation of a databank of measurements during stereotypical actions of daily living. Population recruited (P1 recruits) Patients to complete a demographic questionnaire based on historical falls risk data. Recruited patients allocated to a mobility/independence subgroup based on the information obtained from the demographic questionnaire completed at the time of recruitment. Patients may be reclassified to different subgroups to redistribute them according to other factors including frailty, exercise tolerance, balance etc. Patients given a structured scripted set of simple activities to carry out (such as walking, sitting and picking something off the floor etc.) to cover as many activities of daily of living as possible. Their actions will be monitored with inertia measurement units (IMU) 1-3 per patient (Each will be off the shelf European Conformity (CE) approved products no bigger than a modern smart phone). Data is transmitted from the IMUs wirelessly to the investigators' laptop, patients will not be attached or anchored to any secondary devices or cables. The IMUs are attached to the patients with a comfortable lightweight belt design and or non-invasive leg bands. These scripted sessions will be repeated 5 to 10 times per subject to create an anonymous databank (D1). This will form a reference range per subgroup of truth data for the algorithm to search. Patient identifiable data will be limited to the study questionnaires which will be stored securely at the corresponding study site. Phase 2 - Validation The validation of the databank while observing previously recorded and not previously recorded subjects in an unscripted session. Population patients included All patients from phase 1, new patients recruited (P2 recruits) Newly recruited patients (P2) to complete a demographic questionnaire based on historical falls risk data. These patients will not be immediately allocated to a mobility/independence subgroup, but labelled as unknown (x). All patients observed individually during daily activities in an unscripted session. Patient to wear single IMU sensor which captures mobility data (D2). The activities the subject undertakes during D2 are manually entered onto timesheet by observer for later cross referencing. 2.1 Patients (P2) who are previously unrecorded and who are unallocated to a mobility/independence subgroup have their unscripted data (D2) analysed by the motion algorithm which stratifies them to a mobility/independence subgroup based on the likeness of their data to that stored in the databank (D1). They are then allocated to a mobility/independence subgroup based on their questionnaire data and the two outcomes are compared to see if they match or differ. - Can algorithm predict truth 2.2 Patients (P1) who have previously been recorded in phase 1 and who are allocated to a mobility/independence subgroup based on their questionnaire data have their unscripted data (D2) from phase 2 analysed by the motion algorithm. This then stratifies them again to a mobility/independence subgroup based on the likeness of their new data (D2) to that stored in the databank (D1). To see if their outcome remains the same or differs. - Can algorithm confirm truth (See Appendix B. for diagrammatically representation of phase 1 and phase 2.) During the unscripted session (D2) any falls or patient reported loss of balance that occurs will be recorded and time stamped by the observer. These data sets will be labelled as Falls truth or Balance truth data respectively and added to the anonymous databank for cross-reference.

Official Title
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Remote Monitoring and Analysis of Gait and Falls Within an Elderly Population

Conditions
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Accidental Fall, Fall, Fall Injury, Hip Fractures

Intervention / Treatment
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* Diagnostic Test: CUSH


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Aged over 65 years of age Able to give informed consent Able to mobilise independently or with mobility aid (walking stick, Zimmer frame etc.) Exclusion Criteria: Patients under the age of 65. Patients who are bedbound or wheelchair bound. Patients with cognitive impairment and are unable to give informed consent. Significant medical co-morbidities that make participation in the study unsafe.

Ages Eligible for Study
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Minimum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| 1<br>Capable of independent of daily activities and able to mobilise unaided. No previous of falls. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|
| 2<br>Capable of independent of daily activities and able to mobilise unaided. With a previous of atleast one fall. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|
| 3<br>Requires help with most daily activities, mobilises with a single walking stick. No previous falls. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|
| 4<br>Requires help with most daily activities, mobilises with a single walking stick. With a previous of atleast one fall. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|
| 5<br>Requires help with most daily activities. Mobilise with frame or roller frame. No previous falls. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|
| 6<br>Requires help with most daily activities. Mobilise with frame or roller frame. Previous history of at least one fall. | Diagnostic Test: CUSH<br>* Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Validation of gait analysis algorithm by use of Inertial Measurement Unit (IMU) | Validation of a motion analysis algorithm to predict falls risk in an elderly population using IMU data captured from accelerometers and gyroscopes within the IMU. The IMU will allow capture of data from wearers gait and movement. | 3 months |