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HAL for Medical Use (Lower Limb Type) orthotically fits to the lower limbs and trunk;

HAL is a gait training device intended to temporarily help improve ambulation upon completion of the HAL gait training intervention. HAL must be used with a Body Weight Support system. HAL is not intended for sports or stair climbing. HAL gait training is intended to be used in conjunction with regular physiotherapy.

The device is intended for individuals with:

• spinal cord injury at levels C4 to L5 (ASIA C, ASIA D) and T11 to L5 (ASIA A with Zones of Partial Preservation, ASIA B);

• post stroke paresis

• paraplegia due to progressive neuromuscular diseases (spinal muscular atrophy, spinal and bulbar muscular atrophy, amyotrophic lateral sclerosis, Charcot-Marie-Tooth disease, distal muscular dystrophy, inclusion body myositis, congenital myopathy, muscular dystrophy) who exhibit sufficient residual motor and movement-related functions of the hip and knee to trigger and control HAL

In preparation for HAL gait training, the controller can be used while the exoskeleton is not donned to provide biofeedback training through the visualization of surface electromyography bioelectrical signals recorded.

HAL is intended to be used inside medical facilities while under trained medical supervision in accordance with the user assessment and training certification program

HAL for Medical Use (Lower Limb Type) is a battery powered lower extremity exoskeleton that provides assistive torque at the knee and hip joints for gait training. HAL is comprised of a controller, a main unit, and sensor shoes. The device comes in 8 size variations (4 different leg lengths and 2 different hip widths) for each of the 3 configuration types (doubleleg, right-leg, and left-leg) and weighs ~14 kg (30 lbs). The device uses legally marketed cutaneous electrodes (up to 18 electrodes) to record surface electromyography bioelectrical signals of the hip and knee extensor and flexor muscles when the device is used in Cybernic Voluntary Control (CVC) mode. This mode provides assistive torque at the corresponding ioint (e.g., hip or knee) using surface electromyography bioelectrical signals that are processed using a propriety signal processing algorithm. The propriety processing algorithm allows the device to detect surface electromyography bioelectrical signals to control the HAL device in CVC mode and provide visualization of the surface electromyography bioelectrical signals during biofeedback training. The assistive torque can be adjusted using three parameters: sensitivity level, torque turner, and balance turner. The device can also provide two additional modes: Cybernic Autonomous Control (CAC) mode and Cybernic Impedance Control (CIC) mode. CAC mode provides assistive torque leg trajectories based on postural cues and sensor shoe measurements. CIC mode provides torque to compensate for frictional resistance of the motor based on joint motion. CIC mode does not provide torque assistance for dictating joint trajectories. A trained medical professional (i.e., physician, physical therapist, etc.) can configure, operate, and monitor the device during gait training to make adjustments as needed.

The provided document is a 510(k) Summary for the HAL for Medical Use (Lower Limb Type) device. It describes the device, its intended use, and substantial equivalence to a predicate device (K171909). The document focuses on demonstrating safety and effectiveness, particularly for new patient populations.

It's important to note that this document is an FDA 510(k) summary, which typically presents summarized findings rather than a detailed breakdown of all study methodologies. Therefore, some specific details for each point requested might not be explicitly stated or might require inference from the provided text.

Here's an analysis of the provided information against your requested points:

Acceptance Criteria and Device Performance

The acceptance criteria are not explicitly stated as distinct numerical targets for each performance metric in a single table. Instead, the document demonstrates meeting acceptance criteria through compliance with recognized standards, successful bench testing, and consistent or improved clinical outcomes compared to baseline or control groups across various studies. The "results" sections for non-clinical and clinical data effectively serve as proof of meeting implicit or explicit acceptance criteria related to safety, functionality, and efficacy.

Table of Acceptance Criteria and Reported Device Performance:

Study Details:

This device is a gait training device (exoskeleton), not an AI/imaging device, so many of the requested points related to AI model evaluation, ground truth establishment by experts for image data, MRMC studies, or training/test set sample sizes for an AI algorithm are not directly applicable in the typical sense for this device. The clinical "studies" referred to are more clinical trials or observational studies on human subjects, to demonstrate the effectiveness of the physical device in improving ambulation.

However, I will extract relevant information based on the typical interpretation for evaluating a medical device's performance, applying it to the context of a physical intervention device.

1. A table of acceptance criteria and the reported device performance: refer to the table above.

2. Sample sizes used for the test set and the data provenance:

   • Spinal Cord Injury (SCI) Group (Effectiveness):

     • Sample Sizes: Studies varied from n=8 to n=55. (I-6 studies were assessed for effectiveness). Specific study IDs and their 'n' values:
       • FDA-ID 11 (Aach et al.): n=8
       • FDA-ID 13 (Grasmucke et al.): n=55
       • FDA-ID 17 (Sczesny-Kais): n=11
       • FDA-ID 18 (Jansen et al.): n=21
       • FDA-ID 19 (Jansen et al.): n=8
       • FDA-ID 110 (Puentes et al.): n=12
     • Data Provenance: Not explicitly stated for each study, but the document mentions a "literature search and data held by the manufacturer." The studies are generally chronic SCI patients where spontaneous recovery is not expected, implying these are retrospective analyses of published literature or manufacturer-held data, likely from various international sources (given author names like Aach, Grasmucke, Sczesny-Kais, Jansen, Puentes suggest European/Japanese origins).

   • Stroke Group (Effectiveness):

     • Sample Sizes: Studies varied from n=8 to n=53. (I-14 studies were assessed for effectiveness, categorized by post-stroke stages). Specific study IDs and their 'n' values:
       • I9 (Kawamoto et): n=16 (appears to be a pilot study)
       • I15 (Yoshimoto et): n=18 (for chronic stage)
       • I19 (Tanaka et al.): n=11 (for chronic stage)
       • I20 (Tanaka et al.): n=9 (Chronic, follow-up)
       • I18 (Sczesny-Kais): n=18 (Chronic, crossover RCT)
       • I5 (Nilsson et al.): n=8 (Acute/subacute)
       • I12 (Watanabe et al.): n=22 (Acute/subacute, control group CPT)
       • I14 (Fukuda et al.): n=53 (Acute/subacute)
       • I16 (Tan et al.): n=8 (Acute/subacute)
       • I17 (Puentes et al.): n=11 (Acute/subacute)
       • I11 (Watanabe et al.): n=24 (Acute/subacute, control group CPT)
       • I21 (Yokota et al.): n=37 (Acute stroke rehabilitation)
       • I6 (Yoshikawa et al.): n=16 (End of recovery, comparative study)
       • I13 (Mizukami et al.): n=8 (End of recovery)
     • Data Provenance: Same as SCI group, "literature search and data held by the manufacturer." Given the authors and titles, these are likely retrospective analyses of published literature or manufacturer-held data, likely from various international sources (e.g., Kawamoto, Yoshimoto, Tanaka from Japan; Sczesny-Kais from Europe). One study (I22) and Post-Market Surveillance (PMS) data are specifically mentioned as being from Japan.

   • Progressive Neuromuscular Diseases Group (Effectiveness):

     • Sample Sizes:
       • Literature: 1 case report (I33) with n=3 patients.
       • Clinical Trial: I22, n=24 subjects (investigator-initiated randomized controlled crossover clinical study).
       • Post-Market Surveillance (PMS): n=207 patients (as of November 2019).
     • Data Provenance:
       • Literature: "one published study was assessed."
       • Clinical Trial (I22): Prospective, conducted in Japan, approved by the Ministry of Health, Labour and Welfare of Japan.
       • Post-Market Surveillance: Prospective/Real-World Data, collected over four years in Japan after device approval.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This question is generally for AI/imaging data. For a physical device, "ground truth" is measured by clinical outcomes (e.g., walking speed, distance).
   • The "ground truth" for the clinical performance (gait function) was established through objective functional tests (e.g., 10MWT - 10-meter walk test, 6MWT - 6-minute walk test, 2MWT - 2-minute walk test), performed without the HAL device. These are standard, quantifiable, and objectively measured clinical endpoints.
   • The "experts" involved would be the trained medical professionals (physicians, physical therapists, etc.) who conducted these assessments as part of the clinical studies. Their specific number or qualifications beyond being "medical professionals" are not detailed in this summary, but it's implied they adhere to clinical trial standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • This is typically for image interpretation by multiple readers in diagnostic studies. For this device, the "test set" is patient cohorts undergoing a physical intervention, and outcomes are objective measurements.
   • Not Applicable in the sense of radiological adjudication. The outcome measures are performance-based and objectively quantifiable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC study was not done. This type of study relates to AI assistance for human readers (e.g., radiologists).
   • The document presents clinical studies comparing HAL intervention with baseline (pre-post) or with conventional physical therapy (control group). These are human subjects assisted by the physical device performing a task (walking), not human readers assisted by AI in interpreting data.
   • Effect Size (where applicable for human patients with device assistance vs. without):
     • SCI (10MWT Speed): Improvements ranged from +0.22 m/s to +0.25 m/s. For time-based measures, improvements were around 35.23s faster (e.g., 70.45s to 35.22s).
     • SCI (6MWT Distance): Improvements ranged from +22.75m to +93.2m.
     • Stroke (10MWT Speed - Chronic): Improvements up to +0.21 m/s.
     • Stroke (10MWT Speed - Acute/Subacute - HAL vs. CPT): The comparative study in the end of recovery stage shows that patients initially treated with HAL reached 61.4 ± 26.6 m/min vs. 50.1 ± 25.0 m/min for CPT, with a significant difference (p<0.05). Another study (I12) showed HAL group 10MWT speed improving by +0.24 m/s (from 0.61 to 0.85 m/s) with statistical significance (p<0.05), while the CPT group's improvement was not significant. The summary states: "significant improvements in the HAL group that were not seen in the control group."
     • Progressive Neuromuscular Diseases (2MWT Distance - Clinical Trial): Treatment effect was -10.066 ± 11.062 for 2MWT in the crossover study (P=0.0369 for the difference, implying HAL improved performance relative to control).
     • Progressive Neuromuscular Diseases (PMS Data): Participants showed about +20% difference from baseline function after 1.5 years despite the progressive nature of their disease.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • Not Applicable in the AI sense. This is a physical device that functions with a human in the loop (the patient wearing it, supervised by medical professionals).
   • The "algorithm" mentioned (propriety signal processing algorithm for sEMG) is part of the device's control system, not a standalone diagnostic AI. Its performance is implicitly validated through the overall device's successful operation in non-clinical tests (e.g., torque output, joint angle, sEMG measurement accuracy) and clinical outcomes.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc.):

   • For clinical effectiveness: Objective Outcomes Data from standardized functional tests (10MWT speed/time, 6MWT distance, 2MWT distance, TUG test) performed by patients without the device. These are widely accepted and quantifiable measures of ambulation.
   • For clinical safety: Reported Adverse Events (AEs and SAEs) collection (patient reports, clinician observations).
   • For non-clinical performance: Bench test measurements against predefined engineering specifications and standards.

8. The sample size for the training set:

   • This refers to the dataset used to train an AI model. For this physical device, there isn't a "training set" in this sense for a learned AI algorithm that generates the primary output being evaluated.
   • However, if we broadly consider "training" as the development and validation data, that would encompass results from various engineering tests, and potentially earlier developmental clinical work that informed the device design and control algorithms. The document does not provide a specific "training set" sample size for the device's functional logic, as it's not a machine learning model in the typical sense presented for FDA clearance. The control algorithms are described as "proprietary signal processing algorithm".

9. How the ground truth for the training set was established:

   • Not applicable in the context of an AI training set.
   • The device's control logic (e.g., detecting sEMG to trigger movement) is based on fundamental biomechanical principles and signal processing, validated through the non-clinical tests mentioned (e.g., accuracy of sEMG measurement, joint angle measurement). The "ground truth" for calibrating these systems would involve physical measurements, engineering specifications, and physiological data.

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