Introduction: Transfemoral amputations are known to compromise balance control capabilities, thus increasing the probability of falling. Current research in robotic prostheses is exploring novel strategies to assess the risk of fall and, if required, enable assistive strategies to promote balance recovery.

Objectives: The aim of this study was to identify a set of biomechanical variables associated with fall risk and fall detection in people with transfemoral amputation (pwTFA), which can be potentially assessed through the next generation of sensorized powered prostheses.

Study Design: The study design was scoping review.

Methods: Screening of the literature (CINAHL, PubMed, Scopus, and Web of Science) carried out in February 2020 (quality assessment of retained articles through Downs and Black checklist; retrieved information: measurement tests and biomechanical variables).

Results: From the initial 595 documents, only 25 citations met eligibility criteria. The pwTFA's fall risk assessment and fall detection are usually carried out by using different measurement tests.

Conclusions: Two classes of biomechanical markers for fall risk assessment have the potential to be incorporated in self-contained transfemoral powered prostheses equipped with sensory network and suitable computational capabilities. The first consists of kinematic/kinetic variables of some prosthesis components (e.g., foot acceleration, force loading the pylon, knee angle) to detect either an abrupt lack of balance or a fall-related occurrence as a consequence of knee buckling, slipping, or tripping. The second involves implementing automatic procedures to allow pwTFA to routinely carry out clinical tests, such as the timed up and go test and/or the Four Square Step Test to monitor the decline of sensory motor capabilities, likely documenting an increased fall risk.

Clinical Relevance: The next generation of powered prostheses could both monitor decline on neuromuscular capabilities and gather early signs of an acute lack of balance based on a suitable network of sensors on board. This approach is expected to prevent the fall risk in pwTFA.