The Key Indicator Method for Manual Handling Operations (KIM-LHC) is a standardised ergonomic tool developed by the German Federal Institute for Occupational Safety and Health (BAuA). Motionprint Ergo implements the full KIM-LHC method — automatically extracting posture and movement data from your recording and computing a risk score for every critical lifting moment. This article explains how the scoring system works and how to interpret your results.

1 Overview

The KIM-LHC method evaluates the physical workload and musculoskeletal risk of manual handling tasks — specifically lifting, holding, and carrying. Unlike the NIOSH Lifting Equation, which focuses on a single recommended weight limit, the KIM-LHC method produces a composite risk score by combining four factors: load weight, body posture, task frequency, and additional unfavourable conditions.

The method is designed for workplace-level screening. A high KIM-LHC score does not pinpoint a single cause but indicates that the overall combination of load, posture, and frequency is placing workers at elevated musculoskeletal risk and that task redesign should be considered.

BAuA standard

The KIM-LHC method is published and maintained by the Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA). Motionprint Ergo implements the method according to the current BAuA specification, including gender-differentiated load weight indices and posture classification criteria.

2 Scoring System

The KIM-LHC score is built from four distinct factors. Each factor produces a sub-score; these are combined to give the total risk score for the assessed task.

Load Weight

The weight of the handled object is mapped to a load index using BAuA reference tables. Separate indices apply for male and female workers, reflecting physiological differences in load capacity. Heavier loads produce a higher index, contributing more to the total score.

Body Posture

Start and finish postures for each lifting moment are classified according to BAuA ergonomic criteria. The classification captures trunk bending, lateral inclination, trunk rotation, and hand position relative to the body. Each posture class carries a score reflecting its ergonomic risk level.

Frequency & Duration

The number of handling operations per unit of time is calculated and mapped to a time rating from the BAuA frequency table. Tasks performed more frequently, or sustained over longer durations, receive a higher time rating — acting as a multiplier on the combined load and posture score.

Additional Factors

Extra points are added for unfavourable task conditions that increase musculoskeletal strain beyond what load, posture, and frequency alone capture. These include trunk twisting during the lift, asymmetric load handling, poor grip conditions, and constrained working environments.

3 Calculation

The KIM-LHC total score is computed by combining the four factors using the following structure:

KIM-LHC Total Score Score = (Load Rating + Posture Score + Additional Factors) × Time Rating

Frame-by-frame scoring

Motionprint Ergo calculates KIM-LHC scores at the frame level across the entire recording. For each frame, the relevant joint angles and segment positions are evaluated against the BAuA posture classification thresholds to assign a posture class and additional factor points for that moment.

Critical moments

Rather than averaging across the full recording, the KIM-LHC method focuses on critical moments — the instants during the lift where ergonomic risk is highest. Motionprint Ergo's kimlhc_moment_processor.py identifies these moments automatically using peak detection on the frame-level scores, then aggregates the results across all identified critical moments to produce the final assessment score.

Multi-moment assessments

When a recording contains multiple distinct lifting moments — for example, a worker performing a sequence of pick-and-place tasks — each moment is scored independently. The scores are then combined according to the BAuA aggregation method to produce a single total score representing the overall task risk.

Screenshot
KIM-LHC score breakdown in Motionprint Ergo
Screenshot of the In-Depth Analysis tab showing the individual sub-scores (load rating, posture score, additional factors, time rating) for each identified critical moment and the aggregated total. Provided by client.

4 Action Levels

The total KIM-LHC score maps to one of three action levels, each indicating the degree of musculoskeletal risk and the recommended response:

Score range Action level Recommended action
≤ 50 Low No immediate action required. The physical workload is within acceptable limits for most workers.
50 – 100 Medium Monitor the task and consider ergonomic improvements. The workload may pose elevated risk for some workers, particularly those with reduced physical capacity.
> 100 High Immediate intervention required. Task redesign — such as reducing load weight, improving posture conditions, or reducing frequency — should be implemented before work continues.
Interpreting borderline scores

A score near the boundary between action levels (e.g. 48 or 52) should be treated conservatively. Small changes in load weight, posture, or frequency can shift a task across a threshold. Use the sub-score breakdown in the In-Depth Analysis tab to identify which factor is contributing most to the total and prioritise that for improvement.

5 Implementation in Motionprint Ergo

The KIM-LHC assessment in Motionprint Ergo is implemented in a dedicated Python processing pipeline, applying BAuA posture thresholds and scoring criteria to the motion capture data frame by frame.

Calculation workflow

1
Input data

Joint angles and segment positions are read from the MVNX file. User-defined parameters — load weight, gender, task frequency, and shift duration — are entered in the assessment setup panel and passed to the processing pipeline.

2
Frame-level scoring

kimlhc_score_per_frame.py evaluates posture classification and additional factor conditions at every frame of the recording, producing a per-frame score dataset that feeds into the critical moment detection step.

3
Critical moment detection

kimlhc_moment_processor.py identifies the critical moments within the recording — the frames where ergonomic risk is highest — and extracts the sub-scores (load rating, posture score, additional factors) for each moment.

4
Score aggregation and output

kimlhc_score_overview.py applies the time rating and aggregates scores across all critical moments to produce the final total. Results are presented in the report with the action level, sub-score breakdown, body posture images, and prioritised intervention recommendations.

Report outputs

KIM-LHC results are available in two export formats:

  • PDF report — summarises the total score, action level, sub-score breakdown, and prioritised recommendations. Includes body posture diagrams showing the classified start and finish postures for each critical moment.
  • Excel report — provides a detailed breakdown of all sub-scores, multipliers, and per-moment data, suitable for further analysis or regulatory documentation.
Custom diagram
KIM-LHC body posture diagram
Body posture illustration from the KIM-LHC PDF report showing the classified start and finish postures for a critical moment, with colour-coded risk regions. Provided by client.