SBIR/STTR Research Award

Developing a high sensitivity high specificity lameness detection system for dairy cattle

Award identifier 7a13c279ef5e4a5b5c8cc7f23df32c158f15edbae66050eaf85fbe30e8a698f0Awarding agency Department of Agriculture

Award amount

$100,000

Program and phase

SBIR · Phase I

Award year

2011

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Award details

Awarding agency
Department of Agriculture
Branch
Not reported
Recipient company
STEP ANALYSIS LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
ZZGCSLM1GET6
Solicitation number
Not reported
Topic code
8.3

Research abstract

Bovine lameness has emerged as the second most problematic health and welfare issue for the dairy industry. Prevalence of lameness has steadily risen from 11 to 14 percent over the past 10 years. Culling due to lameness has risen from 13 to 20 percent during the same period. Estimated annual costs of lameness are $511 million because of reproduction inefficiencies, diminished milk yield, and the increased risk of involuntary culling associated with this problem. Part of the difficulty with controlling lameness is the inability to accurately and continuously observe gaits in a herd. Visual detection, though popular, suffers from limited accuracy, reproducibility, and a high labor commitment. Producers as well as highly trained professionals underestimate the prevalence of lameness in herds by as much as 40 percent. Thus, there is a significant need in the dairy industry for automated, objective detection of lameness that will reduce lameness-related losses and generally improve the wellbeing of dairy herds. BouMatic LLC took the lead in commercialization of a one-dimensional (vertical), dual-plate, force sensing technology that can automatically detect lameness in commercial dairy herds (the StepMetrix product). This novel product detects incremental changes in vertical ground reaction forces (GRFs) that are associated with changes in clinical locomotion score and lesion pain as determined by a veterinarian. Use of the one-dimension (vertical) force sensing technology detects changes in gait attributed to lameness but it suffers from limited sensitivity in lameness prediction. Step Analysis LLC has introduced a three-dimensional (vertical, longitudinal, traverse) force sensing system and used it to demonstrate improved accuracy in gait/locomotion assessment over the one-dimensional (vertical) rodent system. Step Analysis proposes to upgrade the bovine system (StepMetrix) from one-dimensional (vertical) force sensing to three-dimensional (vertical, longitudinal and transverse) force sensing which will improve the accuracy of lameness prediction to benefit the dairy industry. Successful completion of Phase I will result in an operational three-dimensional force Sensor Module for StepMetrix and validate the hypothesis that three-dimensional sensing (vertical, longitudinal, traverse) of GRFs can distinguish lame cows from sound cows with misclassification rate of less than or equal to 15 percent. A successful Phase I will pave the way for the development of a statistical model for three-dimensional lameness detection, which will require the testing of several hundred cows. This will be the subject of a Phase II project. We anticipate that the new three-dimensional (3D) StepMetrix will have higher sensitivity and specificity values than the current single-dimensional StepMetrix. This will benefit health of the cows and improve profitability for dairy farmers resulting in the greater market acceptance of automated lameness detection system in the US and globally.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

2 SBIR/STTR records share this UEI (2011–2014). Reported award amounts total $550,000. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Agriculture

Agency intelligence

Department of Agriculture

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