This is a source-verified profile, not a direct interview. Lian Loke is an interdisciplinary artist and interaction-design researcher whose work places lived movement, somatic literacy, and choreographic method at the centre of human–computer and human–robot interaction. Her importance to somatic AI lies in a methodological reversal: begin with what movement feels like and does socially, then decide what a machine should sense.

Movement as material, not merely input

Loke’s official biography describes a practice spanning performance, costume, installation, human–computer interaction, and human–robot interaction. Her current-research statement locates the work in embodied interaction and describes qualitative, design-led, and creative-practice methods that put the lived body at the centre of inquiry.

That position resists a common technical sequence: select a sensor, collect features, then ask what human meaning might fit the output. Loke instead develops movement situations through improvisation, props, costume, somatic practice, and participation. Machine-readable features remain useful, but they enter after the design has encountered a moving person.

“Making strange” as a design method

Loke’s doctoral work, Moving and Making Strange, developed a methodology for movement-based interactive technologies. Making familiar action strange slows down automatic recognition and makes its construction available for reflection. For a designer, an ordinary reach can become a study of initiation, weight, direction, hesitation, relation, and attention rather than a path between two coordinates.

This is not an argument against measurement. It is a method for discovering what should be measured and what may be lost. Somatic-AI Lab’s discussion of why movement scores can miss learning processes applies the same distinction: an observable timing error and a learner’s capacity to recover are related but not identical.

Somatic literacy belongs beside digital literacy

Loke’s research programme describes somatic literacy as a necessary complement to digital design literacy. The phrase gives practitioners a concrete obligation. Knowing how to train a recogniser or tune a responsive mapping does not guarantee an ability to notice how that mapping directs breath, effort, attention, or participation.

Her publications with Claudia Núñez-Pacheco examine felt sense and discernment in interaction design; work with Kristina Höök, Thecla Schiphorst, and other soma-design researchers argues for first-person perspectives in design. First-person evidence is not treated as an escape from rigour. It requires practices for articulation, comparison, and reflective documentation.

Choreography changes how robots are framed

In collaborations with Dagmar Reinhardt, Loke studies human–robot interaction through movement, gesture, touch, and choreographic relations. This shifts the robot from an autonomous performer evaluated only by task completion toward one participant in a spatial and social arrangement.

The distinction is timely. New multiparty motion models represent group and individual states separately, while dance-AI projects examine shared agency. The Lab’s community report on group interaction states shows why choreographic knowledge matters: coordinated action cannot be reconstructed by scoring each body independently.

A productive challenge for somatic AI

Loke’s work suggests that better body models will not, by themselves, produce better embodied interaction. A system may track more joints and still offer a poorer situation for moving, learning, or relating. The design question is therefore not only “what can the model recognise?” but “what kinds of attention and agency does this interaction rehearse?”

That question gives somatic AI a standard beyond technical novelty. It asks researchers to make representation choices visible, include first-person and social evidence, and treat bodies as participants in inquiry rather than sources of training data.

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