An end-to-end system, comprising of a hardware and software platform, for fall risk analysis, fall detection, fall prediction, and fall prevention, physical function, cognitive function, and executive function, wherein the system has a wearable or non-wearable device, comprising:
- one or more sensors for detecting motion from the wearable device or non-wearable device, wherein the wearable device can be fixed or freely moving;
- one or more processors monitoring motion and processing metrics of a user;
- measuring baseline data of physical function, cognitive function, executive function, gait metrics, audio, and health parameters;
- a memory storing one or more sensor data and the baseline data;
- one or more input devices for receiving instructions and feedback from the user;
- one or more output devices providing instructions and feedback to the user;
- an application stored on the wearable device or non-wearable device, or in the back-end, assessing the user's mobility, physical function, cognitive function, and executive function, wherein the application can be performed remotely without clinician oversight;
- the application further comprises a user interface that includes information on health conditions, metabolic conditions, comorbidities and medications taken;
- monitoring user's performance, and assessments of gait;
- wherein the gait measures normal walk, fast walk, dual task walk, sit-to-stand test (STS), timed-up-and-go (TUG), and rotation around hip tests, and identify root causes of a fall risk;
- algorithms assessing mobility, physical function, cognitive function, and executive function using assessments of the gait;
- wherein the gait measures normal walk, fast walk, dual task walk, sit-to-stand test (STS), timed-up-and-go test (TUG), and rotation around hip tests, and other gait metrics, provide fall-risk and fall-risk root cause metrics;
- a fall detection machine learning algorithm identifying a fall;
- a fall prediction machine learning algorithm combining health parameters, cognitive function, executive function, physical function, fall risk metrics and gait metrics, dual task gait, declining gait speed, and other gait metrics over a period of time;
- predicting a fall based on the combined sensor metrics, the fall detection algorithm, the fall prediction algorithm, the gait metrics, health parameter metrics, environmental metrics, and audio processing;
- accessing cognitive and executive function based on audio recordings of the user's performance of dual task gait and daily variations in dual task gait, in addition to gait metrics and physical function;
- a Fractal Scale Index based algorithm to identify gait patterns in ADRD (Alzheimer's Disease and Related Dementias) and cardiovascular health;
- transmitting feedback to the user if the gait and balance metrics is higher than the user's baseline metrics;
- wherein the feedback can be visual or audible;
- transmitting feedback to the user if the physical function of the user is lower than the user's baseline metrics;
- wherein the feedback can be visual or audible;
- transmitting feedback to the user if the cognitive function of the user is lower than the user's baseline metrics;
- wherein the feedback can be visual or audible;
- and
- transmitting feedback to the user if the executive function of the user is lower than the user's baseline metrics;
- wherein the feedback can be visual or audible.