Lifting and Carrying – ISO 11228

Dataset Name: INAIL Lifting Dataset 1 (March 2015)

You can download the dataset HERE.

Research Groups:
INAIL

Data Type: Human Motion Data, Human Postures

Data Structure: Kinematic (optoelectronic), Kinetic (force platforms) and sEMG data

Data Format: .TDF, .mat

Sampling Rate: > 100 Hz

Action Type: lifting a load using both hands




Objects Type: Real Objects

Equipment: Marker Motion Capture System (SMART-DX 6000 System, BTS, Milan, Italy), force platforms (P 6000, BTS, Milan, Italy), surface electromyography (FreeEMG300 System, BTS, Milan, Italy).

# of Actions: 10 subjects x 3 tasks x 3 repetitions of each task



# of Subjects: 10

Dataset Information:

Each subject executed the tasks lifting a load using both hands. The lifting tasks were executed at three different risk levels determined according to the Revised NIOSH Lifting Equation.

















More information can be found on the report that accompanies the dataset.


How to Cite: Results on this dataset (completed or partial) are published in [1-4].


[1] Ranavolo A, Varrecchia T, Rinaldi M, Silvetti A, Serrao M, Conforto S, Draicchio F. Mechanical lifting energy consumption in work activities designed by means of the “revised NIOSH lifting equation”. Ind Health. 2017;55(5):444-454.

[2] Ranavolo A, Varrecchia T, Iavicoli S, Marchesi A, Rinaldi M, Serrao M, Conforto S., Cesarelli M., Draicchio, F. (2018). Surface electromyography for risk assessment in work activities designed using the “revised NIOSH lifting equation”. International Journal of Industrial Ergonomics 2018; 68: 34-45.

[3] Varrecchia T, De Marchis C, Rinaldi M, Draicchio F, Serrao M, Schmid M, Conforto S., Ranavolo A. Lifting activity assessment using surface electromyographic features and neural networks. International Journal of Industrial Ergonomics, 2018; 66: 1-9.

[4] Varrecchia T, De Marchis C, Draicchio F, Schmid M, Conforto S,  Ranavolo A. Lifting activity assessment using kinematic features and neural networks. Applied Sciences, 2020; 10(6): 1989.

Dataset Name: INAIL Lifting Dataset 2 (March 2021)

You can download the dataset HERE.

Research Groups:
INAIL

Data Type: Human Motion Data, Human Postures

Data Structure: Kinematic (optoelectronic), Kinetic (force platforms) and sEMG data

Data Format: .TDF, .mat

Sampling Rate: > 100 Hz

Action Type: lifting a load using both hands by one and two persons



Objects Type: Real Objects

Equipment: Marker Motion Capture System (SMART-DX 6000 System, BTS, Milan, Italy), force platforms (P 6000, BTS, Milan, Italy), surface electromyography (Mini Wave Infinity, Cometa, Città, Italy)

# of Actions: 13 subjects x 3 tasks x 3 repetitions of each task x 2 conditions (one vs. two persons)

# of Subjects: 13

Dataset Information:

Each subject executed the tasks lifting a load using both hands. The lifting tasks were performed in two different task conditions: i) individually (one-person team lifting); ii) with the help of a second person (two-person team lifting). Subjects in two-person team lifting were gender, height, weight, and strength matched within 5%.







More information can be found on the report that accompanies the dataset.

How to Cite: Results on this dataset (completed or partial) are published in [1].


[1] Chini G, Varrecchia T, Tatarelli A, Silvetti A, Fiori L, Draicchio F,  Ranavolo A. Trunk muscle co-activation and activity in one-and two-person lifting. International Journal of Industrial Ergonomics, 2022;  89: 103297.

Dataset Name: INAIL – UT Lifting Dataset 3 (October 2022)

You can download the dataset HERE.

Research Groups: INAIL,UT

Data Type: Human Motion Data, Human Postures

Data Structure: Kinematic (optoelectronic), Kinetic (force platforms) and sEMG data

Data Format: .TDF, .mat

Sampling Rate: > 100 Hz

Action Type: repeatedly lifting a load using both hands with and without a back exoskeleton

Objects Type: Real Objects

Equipment: Marker Motion Capture System (Qualisys Oqus camera system), force platforms (AMTI Force plates), surface electromyography (Delsys, Bagnoli, USA)


# of Actions: 4 subjects x 1 tasks x 2 conditions (with vs. without exoskeleton)


# of Subjects: 4

Dataset Information:

Each subject performed an asymmetric lifting-lowering task. Each subject began the experimental trial by standing between two platforms, one in front at a height of 46.5 cm and the other on the right at a height of 106.5 cm from the ground. The subjects were instructed to move a gear between the two platforms for 7 minutes at 12 bpm. The resting and experimental trials were performed twice, on two separate days, without and with an exosuit (w and wo, respectively).

More information can be found on the report that accompanies the dataset.

How to Cite: Results on this dataset (completed or partial) are in a poster presented at the ICORR Conference 2023, [1].

[1] Refai MIM, Sridar S, Govaerts R, Chini G, Varrecchia T, Del Ferraro S, Falcone T, De Bock S, Molinaro V, Elprama SA, Jacobs A, Ranavolo A, De Pauw K, van der Kooij H, Sartori M. Does a soft actuated back exosuit influence multimodal physiological measurements and user perception during an industry inspired task? Poster presented at ICORR Conference 2023.

Dataset Name: INAIL Lifting Dataset 4 (September 2023)

You can download the dataset HERE.

Research Groups: INAIL

Data Type: Human Motion Data, Human Postures

Data Structure: Kinematic (imus), Kinetic (force platforms)



Data Format: .TDF, .mat, .mvn, .xlsx

Sampling Rate: > 100 Hz

Action Type: repeatedly lifting a load using both hands



Objects Type: Real Objects

Equipment: Xsens MVN Link system (Xsens, Enschede, The Netherlands), force platforms (P 6000, BTS, Milan, Italy)






# of Actions: 10 subjects x 3 tasks x 2 conditions.




# of Subjects: 10

Dataset Information:

Subjects were required to perform repetitive symmetric lifting tasks using both hands for four minutes at two lifting conditions.




















More information can be found on the report that accompanies the dataset.

How to Cite: